Shooting method and device

Abstract

The embodiment of the invention discloses a shooting method and device. After a shooting instruction is received, an image acquisition module is started to acquire continuous initial images, for the photo shooting instruction, a first initial image without exposure extreme is selected from the initial images as a reference image, and a preset number of adjacent initial images are selected after the reference image as target images; and obtaining a first intermediate image after noise reduction according to the target image and the reference image, determining a target brightness value, and performing brightness processing on the first intermediate image according to the target brightness value to obtain a shot photo. Therefore, the first initial image without exposure extreme is used as the reference image to carry out photo shooting processing, so that photo shooting does not need to wait for automatic exposure convergence, shooting delay is reduced, shooting timeliness is improved, meanwhile, additional devices do not need to be added, and equipment cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, and in particular, to a photographing method and device. BACKGROUND

[0002] With the rapid development of computer technology, photographing function has been widely integrated into various devices. In the scenario where a user can initiate a photographing request at any time, it is crucial to quickly complete 3A (Auto Exposure, Auto Focus, Auto White Balance) to capture a key moment.

[0003] In the prior art, one is to initialize current AE (Auto Exposure) by reusing the exposure parameter of the last photographing, but this method can only shorten the AE convergence speed to improve the timeliness of photographing when the scene brightness of the last photographing is close to that of the current photographing, and will increase the photographing delay when the difference between the two scenes is large. Another is to introduce an independent light sensing device to continuously monitor the ambient brightness to provide an initial value for AE, but since the light sensing device and the camera are an open-loop system, the initialization information is still different from the real demand, and there is still a certain photographing delay. At the same time, the increase of the light sensing device also increases the cost. SUMMARY

[0004] Therefore, the embodiments of the present application aim to provide a photographing method and device, which uses the first initial image without exposure extreme as a reference image for photographing processing, so that the photographing does not need to wait for the automatic exposure to converge, reduces the photographing delay, improves the timeliness of photographing, and at the same time, does not need to increase additional devices, and reduces the cost of the device.

[0005] In a first aspect, the embodiments of the present application provide a photographing method, which comprises: starting an image acquisition module to acquire continuous initial images in response to receiving a photographing instruction; selecting the first initial image without exposure extreme as a reference image in the initial images in response to the photographing instruction being a photographing instruction; selecting a predetermined number of adjacent initial images after the reference image as target images; acquiring a first intermediate image after noise reduction according to the target images and the reference image; determining a target brightness value; performing brightness processing on the first intermediate image according to the target brightness value to obtain a photographed photograph.

[0006] In some embodiments, the starting the image acquisition module to acquire continuous initial images in response to receiving the photographing instruction comprises: acquiring the exposure parameter used by the image acquisition module in the last photographing in response to receiving the photographing instruction; initialize the image capture module according to the exposure parameter used by the image capture module last time for image capturing; After the initialization is completed, continuously capture initial images by the image capture module.

[0007] In some embodiments, the capturing of the initial images by the image capture module after the initialization is completed comprises: capturing a first frame of the initial images based on the initialized exposure parameter; adjusting the exposure parameter according to the first frame of the initial images, and capturing a next frame of the initial images based on the adjusted exposure parameter, and repeating the adjustment of the exposure parameter and the capturing of the images until the exposure parameter converges; After the exposure parameter converges, capturing the subsequent initial images based on the converged exposure parameter.

[0008] In some embodiments, the adjusting of the exposure parameter according to the first frame of the initial images comprises: in response to the capturing instruction being a photo capturing instruction, adjusting the exposure parameter by a first exposure algorithm, wherein a debugging parameter of the first exposure algorithm is set to a high-speed mode to accelerate the convergence speed; in response to the capturing instruction being a video capturing instruction, adjusting the exposure parameter by a second exposure algorithm, wherein a debugging parameter of the second exposure algorithm is set to a normal mode.

[0009] In some embodiments, the reference image is obtained by iteratively performing the following steps: selecting an initial image as a candidate image according to an order of the initial images; determining an exposure score corresponding to the candidate image; in response to the exposure score being greater than or equal to a first threshold value and less than or equal to a second threshold value, determining the candidate image as the reference image; in response to the exposure score being less than the first threshold value or the exposure score being greater than the second threshold value, selecting a next frame of the initial images as the candidate image.

[0010] In some embodiments, the obtaining of the first intermediate image after noise reduction according to the target image and the reference image comprises: calculating alignment parameters of each of the target images and the reference image; transforming the target images according to the alignment parameters; fusing the transformed target images and the reference image to obtain the first intermediate image.

[0011] In some embodiments, the determining of the target brightness value comprises: obtaining a pre-set target brightness value; or The target brightness value is determined according to an initial image captured after convergence of the exposure parameter.

[0012] In some embodiments, the method further comprises: In response to the photographing instruction being a video photographing instruction, performing noise reduction processing on each initial image to obtain a second intermediate image; obtaining a target brightness value of each initial image; performing brightness processing on the second intermediate image according to the target brightness value to obtain a video frame; generating a photographed video according to the video frame.

[0013] In some embodiments, the noise reduction processing on each initial image to obtain a second intermediate image comprises: selecting a current frame initial image to be processed in the initial images in sequence; selecting a predetermined number of adjacent initial images after the current frame initial image as target images; obtaining a second intermediate image after noise reduction according to the current frame initial image and the target images.

[0014] In some embodiments, the obtaining of the target brightness value of each initial image comprises: for a first initial image, obtaining a preset target brightness value or determining the target brightness value according to an initial image captured after convergence of the exposure parameter; for a second initial image to a last initial image before convergence of the exposure parameter, obtaining a brightness value of each initial image, and filtering the brightness value of each initial image to obtain a target brightness value of each initial image.

[0015] In a second aspect, an embodiment of the present application provides a photographing device, and the device comprises: an instruction receiving unit configured to start an image collecting module to collect continuous initial images in response to receiving a photographing instruction; a reference image determining unit configured to select a first initial image without an exposure extreme as a reference image in the initial images in response to the photographing instruction being a photo photographing instruction; a target image determining unit configured to select a predetermined number of adjacent initial images after the reference image as target images; a first intermediate image obtaining unit configured to obtain a first intermediate image after noise reduction according to the target images and the reference image; a target brightness value determining unit configured to determine a target brightness value; a photo generating unit configured to perform brightness processing on the first intermediate image according to the target brightness value to obtain a photographed photo.

[0016] Thirdly, embodiments of the present invention provide an imaging device, including an image acquisition module, a memory, and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method as described in the first aspect.

[0017] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the method described in the first aspect.

[0018] Fifthly, embodiments of the present invention provide a computer program product comprising a computer program, wherein when the computer program is run on a computer, the computer performs the method described in the first aspect above.

[0019] The technical solution of this invention involves activating an image acquisition module to acquire a series of initial images upon receiving a shooting command. For a photo shooting command, the first initial image without exposure extremes is selected as a reference image. A predetermined number of adjacent initial images are selected as target images after the reference image. A noise-reduced first intermediate image is obtained based on the target image and the reference image. A target brightness value is determined, and brightness processing is applied to the first intermediate image based on the target brightness value to obtain the captured photo. Therefore, by using the first initial image without exposure extremes as a reference image for photo shooting processing, photo shooting does not require waiting for automatic exposure convergence, reducing shooting delay and improving shooting timeliness. Simultaneously, no additional components are needed, reducing equipment costs. Attached Figure Description

[0020] The above and other objects, features and advantages of the present invention will become clearer from the following description of embodiments of the invention with reference to the accompanying drawings, in which: Figure 1 This is a schematic diagram of the shooting device according to an embodiment of the present invention; Figure 2 This is a flowchart of the shooting method according to the first embodiment of the present invention; Figure 3 This is a flowchart of the initial image acquisition process according to an embodiment of the present invention; Figure 4 This is a flowchart illustrating the acquisition of continuous initial images according to an embodiment of the present invention; Figure 5 This is a flowchart of obtaining a reference image according to an embodiment of the present invention; Figure 6 This is a flowchart of obtaining the first intermediate image according to an embodiment of the present invention; Figure 7This is a timeline diagram of photo capture according to an embodiment of the present invention; Figure 8 This is a flowchart of the shooting method according to the second embodiment of the present invention; Figure 9 This is a flowchart of obtaining the second intermediate image according to an embodiment of the present invention; Figure 10 This is a timing diagram of video capture according to an embodiment of the present invention; Figure 11 This is a schematic diagram of the imaging device according to an embodiment of the present invention. Detailed Implementation

[0021] The present application is described below based on embodiments, but it is not limited to these embodiments. In the detailed description of the present application below, certain specific details are described in detail. Those skilled in the art can fully understand the present application without these details. To avoid obscuring the substance of the present application, well-known methods, processes, flows, elements, and circuits are not described in detail.

[0022] Furthermore, those skilled in the art should understand that the accompanying drawings provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0023] Unless the context explicitly requires it, words such as "including" or "contains" throughout the application should be interpreted as including rather than exclusive or exhaustive; that is, meaning "including but not limited to".

[0024] In the description of this application, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0025] The solutions described in this specification and embodiments, if involving the processing of personal information, will be processed only on the premise of having a legal basis (such as obtaining the consent of the personal information subject, or being necessary for the performance of a contract), and will only be processed within the scope stipulated or agreed upon. A user's refusal to process personal information beyond what is necessary for basic functions will not affect the user's use of basic functions.

[0026] With the rapid development of computer vision, embedded systems, and artificial intelligence technologies, image acquisition has been deeply integrated into various smart hardware devices such as smartphones, wearable devices, in-vehicle systems, and even IoT terminals. Especially in the emerging field of wearable devices, represented by smart glasses, the shooting function has become a core capability for users to record their lives and assist in interaction. However, limited by device size, power consumption budget, and the highly dynamic nature of usage scenarios, achieving fast, stable, and high-quality image capture when users spontaneously initiate a photo-taking command has become a critical technical bottleneck that current intelligent imaging systems urgently need to overcome.

[0027] In modern digital imaging workflows, the 3A (Auto Exposure, Auto Focus, and Auto White Balance) algorithm is a fundamental step in ensuring image quality. Among these, Auto Exposure (AE), a crucial component of 3A, directly impacts image brightness, contrast, and signal-to-noise ratio. Its convergence speed directly determines the delay between the user triggering the shutter and the actual image formation. Ideally, the system should immediately complete the stable configuration of the 3A parameters and output a usable image the instant the user presses the shutter button. However, in real-world applications, especially in resource-constrained wearable devices such as smart glasses, this goal faces significant challenges.

[0028] Currently, mainstream AE initialization strategies mainly fall into two categories: First, reusing the exposure parameters obtained from the previous shot as the initial value for the current frame. This method can indeed significantly shorten the AE convergence time and improve response speed when shooting similar lighting scenes continuously; however, once the user moves from indoors to outdoors, or from a low-light environment to a bright light environment, the historical parameters will deviate significantly from the actual needs of the current scene, causing the AE algorithm to fall into an incorrect search starting point, prolonging the convergence process, and even triggering multiple oscillations and adjustments, resulting in significant shooting delays and missing crucial shots. Second, some devices introduce independent ambient light sensors to provide prior information for AE by continuously monitoring ambient illuminance. Although this approach can improve the accuracy of initial exposure estimation to some extent, due to the fundamental differences between ambient light sensors and image sensors in terms of optical path, spectral response, dynamic range, and spatial field of view, they form an open-loop system. The brightness information provided by the ambient light sensor cannot truly reflect the scene content actually seen by the camera, especially under complex lighting conditions (such as local strong light, backlight, and mixed light sources), the error is significant, and it is still difficult to avoid the delay caused by AE recalibration. Furthermore, adding dedicated photosensitive devices not only increases hardware costs and system complexity, but also contradicts the design philosophy of miniaturized, low-power devices such as smart glasses.

[0029] More importantly, as a typical wearable device that may be used at any time, smart glasses users often have sudden shooting intentions while walking, talking, or performing tasks, requiring the system to have near-zero latency response capabilities. If the traditional working mode of sequentially executing on-demand activation, 3A convergence, and imaging is adopted, it usually takes a long time from waking up the camera to completing 3A stabilization, which can easily lead to the permanent loss of precious moments. On the other hand, if the camera is kept in an active state for a long time in pursuit of low latency, it will bring an unacceptable power consumption burden, severely shorten the device's battery life, and violate the basic usage logic of wearable devices.

[0030] Therefore, this invention provides a shooting method to solve the above problems.

[0031] Figure 1 This is a schematic diagram of the imaging device according to an embodiment of the present invention. Figure 1 As shown, the imaging device 1 of this embodiment includes an image acquisition module 14 for acquiring an initial image. The imaging device 1 includes at least one processor 11; a memory 12 communicatively connected to at least one processor 11; and a communication component 13 communicatively connected to a scanning device, the communication component 13 receiving and transmitting data under the control of the processor 11; wherein the memory 12 stores instructions executable by at least one processor 11, the instructions being executed by at least one processor 11 to implement the imaging method of this embodiment.

[0032] In this embodiment of the invention, the shooting device 1 can be any device with shooting function, such as a camera, a mobile terminal (tablet computer, mobile phone, etc.), a wearable device (smart glasses, smart watch, smart bracelet, smart helmet, etc.), a robot, etc.

[0033] Figure 2 This is a flowchart of the shooting method of the first embodiment of the present invention. Figure 2 The illustrated embodiment is applicable to photo shooting scenarios and specifically includes the following steps: Step S110: In response to receiving the shooting command, start the image acquisition module to acquire continuous initial images.

[0034] In this embodiment, when a user has a shooting need, the shooting function of the shooting device is triggered by pressing a button, voice wake-up, or other means. After receiving the shooting command input by the user, the shooting device controls the image acquisition module to acquire continuous initial images.

[0035] The shooting instructions include photo shooting instructions and video shooting instructions. That is, when a user needs to take a photo, they enter a photo shooting instruction; when a user needs to take a video, they enter a video shooting instruction.

[0036] For a photo capture command, the image acquisition module is activated to acquire a predetermined number of consecutive initial images. The predetermined number can be set according to the actual scene.

[0037] For video recording commands, the image acquisition module is activated to capture a series of initial images until a stop command is received.

[0038] Figure 3 This is a flowchart illustrating the acquisition of the initial image according to an embodiment of the present invention. Figure 3 As shown, the step of activating the image acquisition module to acquire continuous initial images in response to receiving a shooting command includes the following steps: Step S111: In response to receiving the shooting command, obtain the exposure parameters used by the image acquisition module in the last shooting.

[0039] In this embodiment, after each shot, the imaging device records the exposure parameters used in that shot, including shutter speed, aperture, and ISO (sensitivity). Shutter speed is a quantifiable parameter of exposure duration, referring to the time interval from the opening to the closing of the camera's focal plane shutter, i.e., the effective duration of light exposure for the image sensor. The aperture is formed by a variable aperture stop within the lens, and its size is determined by the relative aperture. ISO sensitivity is a scale defined by the International Organization for Standardization (ISO) used to quantify the overall sensitivity of the image sensor and signal processing chain; it represents the mapping relationship from exposure to the digital brightness value of the output image. In response to receiving a shooting command input by the user, the exposure parameters used in the previous shot by the image acquisition module are retrieved.

[0040] Step S112: Initialize the image acquisition module according to the exposure parameters used in the previous image capture.

[0041] In this embodiment, the image acquisition module is initialized according to the exposure parameters used in the previous capture, that is, the exposure parameters of the image acquisition module are initialized to the exposure parameters used in the previous capture.

[0042] Step S113: After initialization, acquire continuous initial images through the image acquisition module.

[0043] In this embodiment, after the image acquisition module is initialized, continuous initial images are acquired through the image acquisition module.

[0044] Specifically, Figure 4 This is a flowchart illustrating the acquisition of continuous initial images according to an embodiment of the present invention. For example... Figure 4As shown, the process of acquiring continuous initial images through the image acquisition module after initialization includes the following steps: Step S1131: Capture the first frame of the initial image based on the initialized exposure parameters.

[0045] In this embodiment, after the image acquisition module is initialized, the first frame of the initial image is captured based on the initialized exposure parameters.

[0046] Step S1132: Adjust the exposure parameters according to the first frame initial image, and take the next frame initial image based on the adjusted exposure parameters. Repeat the exposure parameter adjustment and image taking until the exposure parameters converge.

[0047] In this embodiment, the exposure parameters are adjusted based on the first initial image, and the next initial image is captured based on the adjusted exposure parameters. The exposure parameters are adjusted once after each initial image is captured, until the exposure parameters converge, at which point the exposure parameters are no longer adjusted.

[0048] Specifically, after capturing an initial image, the brightness information corresponding to the initial image is calculated. The calculation of the brightness information can be implemented in various ways, and the embodiments of the present invention do not limit this.

[0049] In one specific implementation, the brightness information of the initial image can be calculated as follows: First, the initial image is converted to grayscale or YUV / YCbCr format, from the RGB color space, to extract the brightness component Y, which is relevant to human visual perception. Then, brightness calculations are performed on the entire image or specific regions (e.g., center-weighted, multi-region weighted, face-priority, etc.), such as calculating average brightness, histogram, and overexposed / underexposed pixel ratio. This step is performed for each grid or selected specific region, ultimately obtaining the brightness information for each region. Finally, the brightness information calculated in the above two steps is weighted and summed to obtain the final brightness value corresponding to the initial image.

[0050] After obtaining the brightness value corresponding to the initial image, the difference between the target value and the calculated brightness value is used to obtain the brightness deviation. Here, the target value is a preset value.

[0051] Based on the aforementioned brightness deviation, the exposure parameters are adjusted using a predetermined control algorithm. This predetermined control algorithm can be a PID (Proportional-Integral-Derivative) control algorithm. The formula for the PID control algorithm is: in, This represents the adjustment value of the exposure parameters corresponding to the initial image of the nth frame. The brightness deviation corresponding to the initial image of the nth frame. For pre-set debugging parameters, The brightness deviation corresponds to the initial image of the (n-1)th frame. The time interval between two consecutive initial images.

[0052] Then, using the adjusted exposure parameter value and the exposure parameter of the current initial image, the exposure parameter used to capture the next initial image is calculated, as shown in the following formula: in, The exposure parameters used to capture the initial image of frame (n+1). The exposure parameters used to capture the initial image of the nth frame. This is the adjustment value for the exposure parameters corresponding to the initial image of the nth frame.

[0053] Therefore, the exposure parameters are updated after each initial image frame is captured, until the exposure parameters converge. For example, convergence is considered achieved when the brightness deviation corresponding to multiple consecutive initial images is less than a predetermined threshold.

[0054] Furthermore, in response to the shooting command being a photo shooting command, the exposure parameters are adjusted using a first exposure algorithm, wherein the tuning parameters of the first exposure algorithm are set to high-speed mode to accelerate the convergence speed. That is, the aforementioned tuning parameters... Set to high-speed mode to accelerate convergence. Specifically, adjust the parameters... Setting it to a larger value activates high-speed mode.

[0055] In response to the shooting command being a video shooting command, the exposure parameters are adjusted using a second exposure algorithm, and the adjustment parameters of the second exposure algorithm are set to normal mode. That is, the aforementioned adjustment parameters are... Set to normal mode. Specifically, adjust the debug parameters. Setting the value to near the normal value constitutes the normal mode. The normal and larger values ​​can be set based on experience.

[0056] Step S1133: After the exposure parameters converge, take an initial image based on the converged exposure parameters.

[0057] In this embodiment, after the exposure parameters converge, the initial image is captured again using the converged exposure parameters.

[0058] In summary, after initializing the image acquisition module, a series of initial images are acquired through it. Specifically, after initializing the image acquisition module, firstly, a first frame of initial image is captured based on the initialized exposure parameters. Then, the exposure parameters are adjusted based on the first frame of initial image, and the next frame of image is captured based on the adjusted exposure parameters. The exposure parameters are adjusted once after each frame is captured. This process continues until the exposure parameters converge, at which point no further adjustments are made, and subsequent initial images are captured using the converged exposure parameters. This process yields a series of initial images.

[0059] Step S120: In response to the shooting instruction being a photo shooting instruction, the first initial image without exposure extremes is selected as the reference image from the initial images.

[0060] In this embodiment, in response to the shooting command being a photo shooting command, the first initial image without exposure extremes is selected as the reference image from the initial images.

[0061] Specifically, Figure 5 This is a flowchart illustrating the acquisition of a reference image according to an embodiment of the present invention. For example... Figure 5 As shown, in response to the shooting command being a photo shooting command, selecting the first initial image without exposure extremes as the reference image from the initial images includes the following steps: Step S121: Select an initial image as a candidate image according to the order of the initial images.

[0062] In this embodiment, in the scenario of taking a photo, the initial image further back in the frame is often closer to the user's desired shooting effect. However, since the initial images further back are more prone to extreme exposure, these extremely exposed initial images often do not achieve good results after restoration. Therefore, this embodiment selects the first initial image without exposure extremes as the reference image. Starting from the first frame of initial images, an initial image is selected as a candidate image according to the order of the initial images. That is, the first frame of initial images is selected first.

[0063] Step S122: Determine the exposure score corresponding to the candidate image.

[0064] In this embodiment, the exposure score corresponding to the candidate image is calculated. Specifically, the brightness value of the candidate image is calculated in a similar manner to that in step S1132 above, and the calculated brightness value is used as the exposure score.

[0065] Step S123: In response to the exposure score being greater than or equal to a first threshold and less than or equal to a second threshold, the candidate image is determined as a reference image.

[0066] Step S124: In response to the exposure score being less than the first threshold or the exposure score being greater than the first threshold, select the initial image of the next frame as the candidate image.

[0067] In this embodiment, exposure conditions can be categorized into three types: overexposure, underexposure, and normal exposure. Overexposure and underexposure are both extreme exposures. Therefore, a lower first threshold is set as the underexposure threshold, and a higher second threshold is set as the overexposure threshold. If the exposure score is greater than or equal to the first threshold and less than or equal to the second threshold, it is considered normal exposure, and the candidate image is determined as the reference image. If the exposure score is less than the first threshold, it is considered underexposure; or if the exposure score is greater than the second threshold, it is considered overexposure. The initial image of the second frame is selected as the candidate image, and the above determination process continues until the reference image is obtained.

[0068] Step S130: Select a predetermined number of adjacent initial images as target images after the reference image.

[0069] In this embodiment, a predetermined number of adjacent initial images are selected as target images after the reference image. For example, the predetermined number can be 1, 2, 3, etc.

[0070] Step S140: Obtain the first intermediate image after noise reduction based on the target image and the reference image.

[0071] In this embodiment, a first intermediate image after noise reduction is obtained based on the acquired target image and the reference image.

[0072] Specifically, Figure 6 This is a flowchart illustrating the acquisition of the first intermediate image according to an embodiment of the present invention. Figure 6 As shown, obtaining the first intermediate image after noise reduction based on the target image and the reference image includes the following steps: Step S141: Calculate the alignment parameters between each target image and the reference image.

[0073] In this embodiment, alignment parameters can be calculated using an image pyramid. An image pyramid is a multi-scale image representation method that uses a series of images at different resolutions to represent the same scene. These images are stacked in order of scale from coarse to fine (or from low to high resolution), resembling a pyramid. The image pyramid can be a Gaussian pyramid or a Laplacian pyramid. A Gaussian pyramid is obtained by repeatedly applying Gaussian blur and downsampling (usually halving each dimension) to the original image, with each layer having a lower resolution and less detail than the previous layer. A Laplacian pyramid is constructed from a Gaussian pyramid to preserve detail information; each layer is the difference between a layer in the Gaussian pyramid and its upsampled version, and the next layer. For a Gaussian pyramid, the construction process is as follows: input the current frame image (i.e., layer 0); apply Gaussian blur (e.g., using a 5×5 Gaussian kernel) to the current layer image to suppress high-frequency information and prevent aliasing during downsampling. The blurred image is downsampled, for example, by retaining even-numbered rows and columns, or by taking every other pixel, thus halving the image size (width and height each become half of their original values). The downsampled result is used as the next layer. The Gaussian blur and downsampling process is repeated, with each iteration yielding one layer, until the preset number of layers is reached.

[0074] After obtaining the image pyramids corresponding to the target image and the reference image, alignment parameters can be calculated based on the image pyramids. These alignment parameters are the transformation parameters used when converting the target image to the reference image, such as translation or rotation.

[0075] Specifically, starting from the top layer of the image pyramid, the initial transformation parameters (such as translation and rotation) for transforming the target image into the reference image are calculated at the top layer as the alignment result. This calculation can be achieved through methods such as feature point matching or phase correlation. For feature point matching, significant feature points (such as corners and edges) in the two images are detected to establish a correspondence between feature points. Then, the overall translation is estimated by statistically analyzing the median or mean displacement of all matched point pairs. For phase correlation, the cross-power spectrum of the two images is calculated and an inverse Fourier transform is performed, generating a sharp pulse peak in the correlation plane. The position of this peak directly corresponds to the translation vector.

[0076] After obtaining the initial transformation parameters, optimization begins progressively from the higher to the lower layers. First, since the scale difference between image pyramid layers is 2, the initial transformation parameters (such as translation) need to be scaled up proportionally. Then, a function is defined to measure the alignment quality of the two images; for example, the sum of squared grayscale differences can be used, which minimizes the brightness difference between corresponding pixels in the two images. Next, the gradient of the objective function with respect to the translation parameters is calculated. Optimization methods such as gradient descent and Gauss-Newton's method are used to update the translation parameters with a certain step size based on the gradient direction. When the change in parameters or the change in the objective function value is less than a set threshold, the current layer is considered converged, and the process moves to the next layer for further optimization. This process continues until the bottom layer is reached; the alignment result at the bottom layer is the alignment parameter.

[0077] Step S142: Transform the target image according to the alignment parameters.

[0078] In this embodiment, based on the alignment parameters obtained in the previous step, the target image is transformed using these parameters. During the transformation process, pixel positions in the target image may map to non-integer positions in the new image. In this case, interpolation methods (such as nearest neighbor interpolation, bilinear interpolation, bicubic interpolation, etc.) can be used to estimate the pixel values ​​at these positions. In the transformed image, some areas may not correspond to any pixels in the original image. In this case, a specific color (such as black or white) can be chosen to fill the area, or the values ​​of surrounding pixels can be used to fill it. Thus, the transformed target image can be obtained.

[0079] Step S143: Fuse the transformed target image with the reference image to obtain the first intermediate image.

[0080] In this embodiment, the transformed target image is fused with the reference image to obtain the first intermediate image. The image fusion algorithm can be implemented using various existing methods, such as averaging or weighted methods.

[0081] For the averaging method, the corresponding pixel values ​​of the target image and the reference image are averaged to obtain the first intermediate image.

[0082] For the weighted method, the corresponding pixel values ​​of the target image and the reference image are weighted and averaged to obtain the first intermediate image.

[0083] It should be understood that, in addition to the fusion algorithms listed above, fusion can also be achieved through transform domain-based methods, multi-resolution analysis-based methods, or deep learning-based methods.

[0084] Therefore, the content of the reference image can be restored using the above method.

[0085] Step S150: Determine the target brightness value.

[0086] In this embodiment, the target brightness value can be preset or determined based on the initial image captured after the exposure parameters converge.

[0087] When determining the target brightness value based on the initial image taken after the exposure parameters converge, a random selection is made from the initial images taken after the exposure parameters converge (for example, the first initial image taken after the exposure parameters converge), and the brightness value corresponding to the initial image is calculated (which can be calculated using the brightness value calculation method in step S1132 above). This brightness value is then determined as the target brightness value.

[0088] Step S160: Perform brightness processing on the first intermediate image according to the target brightness value to obtain the captured photograph.

[0089] In this embodiment, the first intermediate image is processed for brightness based on the target brightness value to obtain the captured photograph.

[0090] Brightness processing can be implemented using various existing methods, and this embodiment of the invention does not impose any limitations on this. For example, the Gamma method can be used for brightness processing. Gamma correction is a non-linear brightness adjustment method that adjusts brightness by exponentially transforming the image pixel values. This method is more in line with the human eye's perception of brightness and can maintain the overall contrast and detail of the image.

[0091] Specifically, firstly, the actual brightness value of the first intermediate image is calculated (which can be calculated using the brightness value calculation method in step S1132 above), denoted as L. C The target brightness value obtained above is denoted as L. T Based on the actual brightness value L C and target brightness value L T The specific formula for calculating the Gamma value is as follows: Then, the pixel values ​​in the first intermediate image are normalized to the range of 0 to 1, and an exponential operation is performed on each pixel value, with the calculation formula as follows: Thus, the photographs can be obtained.

[0092] In addition to brightness processing, the brightness-processed image can also undergo ISP (Image Signal Processor) processing to obtain the captured photograph. The ISP is a dedicated hardware module, typically integrated into a SoC (System-on-a-Chip), which primarily performs de-mosaic, white balance, color correction, noise reduction, automatic exposure / focus control, and image enhancement on the first intermediate image sequence.

[0093] Furthermore, Figure 7 This is a timing diagram of the photographs taken according to an embodiment of the present invention. For example... Figure 7 As shown: At time t0, after receiving the user's shooting command, the image acquisition unit is initialized. Specifically, the image acquisition module is initialized according to the exposure parameters used in the previous shooting, that is, the exposure parameters of the image acquisition module are initialized to the exposure parameters used in the previous shooting.

[0094] Initialization is completed at time t1, that is, the initialization phase is between t0 and t1.

[0095] After initialization, at time t1, the first initial image F1 is captured based on the initialized exposure parameters. Then, the exposure parameters are adjusted based on the first initial image F1, and the next image is captured based on the adjusted exposure parameters. The exposure parameters are adjusted once after each image is captured.

[0096] At time t2, after capturing the Mth frame, exposure parameter convergence is detected, and the Mth frame is set as the first converged initial frame. The period between t1 and t2 is the AE convergence phase.

[0097] In the scenario of taking photos, the shooting can end after t2, or a predetermined number of images can continue to be taken. This embodiment of the invention does not impose any restrictions on this.

[0098] In the captured initial images, starting from the first initial frame, the first initial image without exposure extremes is selected as the reference image according to its attributes. Typically, the first initial frame may exhibit extreme exposure, but the probability of extreme exposure is extremely low starting from the second initial frame. Therefore, when the first initial frame has no extreme exposure, the reference image is used as the first initial frame, and the captured image obtained through the above processing is the content of the first initial frame. In this case, the delay between the user triggering the photo capture and the capture of the content is only the time interval t0-t1. However, existing technologies use the captured content after AE convergence as the photo content; that is, in existing technologies, the delay between the user triggering the photo capture and the capture of the content is the time interval t0-t2. Therefore, the shooting method of this embodiment can significantly reduce shooting delay. Ideally, it can reduce the entire AE convergence phase time. In addition, even if the first initial image has extreme exposure, the possibility of the second initial image having extreme exposure is extremely small. In this case, choosing the second initial image as the reference image, although it increases the delay time compared to the ideal situation, the increase in delay is very small. Compared with existing technologies, it can still greatly reduce the delay.

[0099] In other words, by skipping the AE convergence process during camera cold start and starting shooting directly, the algorithm uses the frame before AE convergence as the content reference frame and the frame after AE convergence as the brightness reference frame. It also uses the frames adjacent to the content reference frame to restore the dynamic range and signal-to-noise ratio of the content reference frame. Finally, the algorithm processes the data to obtain a shooting frame with a signal-to-noise ratio, dynamic range, and brightness that are equivalent to the system target value. This algorithm can be deployed in real time in the RAW (raw image) domain and can also be deployed in the YUV (color encoding method) domain for post-processing.

[0100] This invention, upon receiving a shooting command, activates an image acquisition module to acquire a series of initial images. For a photo shooting command, the first initial image without exposure extremes is selected as a reference image. A predetermined number of adjacent initial images are selected as target images after the reference image. A noise-reduced first intermediate image is obtained based on the target image and the reference image. The target brightness value is determined, and brightness processing is applied to the first intermediate image based on the target brightness value to obtain the captured photo. Therefore, by using the first initial image without exposure extremes as a reference image for photo shooting processing, photo shooting does not require waiting for automatic exposure convergence, reducing shooting delay and improving shooting timeliness. Simultaneously, no additional components are needed, reducing equipment costs.

[0101] Figure 8 This is a flowchart of the shooting method according to the second embodiment of the present invention. Figure 8The illustrated embodiment is applicable to video shooting scenarios and specifically includes the following steps: Step S210: In response to receiving the shooting command, start the image acquisition module to acquire continuous initial images.

[0102] The process of acquiring continuous initial images can be referred to step S110 above, and will not be repeated here in this embodiment of the invention.

[0103] Step S220: In response to the shooting command being a video shooting command, noise reduction processing is performed on each initial image to obtain a second intermediate image.

[0104] In this embodiment, in response to the shooting command being a video shooting command, noise reduction processing needs to be performed on each initial image to obtain a second intermediate image.

[0105] Specifically, Figure 9 This is a flowchart illustrating the acquisition of the second intermediate image according to an embodiment of the present invention. For example... Figure 9 As shown, in response to the shooting command being a video shooting command, the noise reduction processing of each initial image to obtain the second intermediate image includes the following steps: Step S221: Select the current frame initial image to be processed from the initial images in sequence.

[0106] In this embodiment, in response to the shooting instruction being a video shooting instruction, noise reduction processing is required for each initial image. First, starting from the first initial image, the current frame initial image to be processed is selected sequentially from the initial images.

[0107] Step S222: Select a predetermined number of adjacent initial images as target images after the initial image of the current frame.

[0108] In this embodiment, a predetermined number of adjacent initial images are selected as target images after the initial image of the current frame. For example, the predetermined number can be 1, 2, 3, etc.

[0109] Step S223: Obtain the second intermediate image after noise reduction based on the initial image of the current frame and the target image.

[0110] In this embodiment, a second intermediate image after noise reduction is obtained based on the initial image of the current frame and the target image.

[0111] Specifically, the alignment parameters between each target image and the initial image of the current frame are first calculated. In this embodiment, the alignment parameters can be calculated using an image pyramid. An image pyramid is a multi-scale image representation method that uses a series of images at different resolutions to represent the same scene. These images are stacked in order of scale from coarse to fine (or from low to high resolution), resembling a pyramid. The image pyramid can be a Gaussian pyramid or a Laplacian pyramid. A Gaussian pyramid is obtained by repeatedly applying Gaussian blur and downsampling (usually halving each dimension) to the original image, with each layer having a lower resolution and less detail than the previous layer. A Laplacian pyramid is constructed from a Gaussian pyramid and is used to preserve detailed information; each layer is the difference between a layer in the Gaussian pyramid and its upsampled version with the next layer. For a Gaussian pyramid, the construction process is as follows: Input the current frame image (i.e., layer 0); apply Gaussian blur to the current layer image (e.g., using a 5×5 Gaussian kernel) to suppress high-frequency information and prevent aliasing during downsampling. Downsample the blurred image, for example, by retaining even-numbered rows and columns, or by taking one pixel every other pixel, halving the image size (width and height each become half of their original values), and use the downsampled result as the next layer. Repeat the above Gaussian blur and downsampling process, obtaining one layer each time, until the preset number of layers is reached.

[0112] After obtaining the image pyramids corresponding to the target image and the initial image of the current frame, alignment parameters can be calculated based on the image pyramids. These alignment parameters are the transformation parameters used when converting the target image to the initial image of the current frame, such as translation or rotation.

[0113] Specifically, starting from the top layer of the image pyramid, the initial transformation parameters (such as translation and rotation) for transforming the target image to the initial image of the current frame are calculated at the top layer as the alignment result. This calculation can be achieved through methods such as feature point matching or phase correlation. For feature point matching, significant feature points (such as corners and edges) in the two images are detected to establish a correspondence between feature points. Then, the overall translation is estimated by statistically analyzing the median or mean displacement of all matched point pairs. For phase correlation, the cross-power spectrum of the two images is calculated and inverse Fourier transform is performed, generating a sharp pulse peak in the correlation plane. The position of this peak directly corresponds to the translation vector.

[0114] After obtaining the initial transformation parameters, optimization begins progressively from the higher to the lower layers. First, since the scale difference between image pyramid layers is 2, the initial transformation parameters (such as translation) need to be scaled up proportionally. Then, a function is defined to measure the alignment quality of the two images; for example, the sum of squared grayscale differences can be used, which minimizes the brightness difference between corresponding pixels in the two images. Next, the gradient of the objective function with respect to the translation parameters is calculated. Optimization methods such as gradient descent and Gauss-Newton's method are used to update the translation parameters with a certain step size based on the gradient direction. When the change in parameters or the change in the objective function value is less than a set threshold, the current layer is considered converged, and the process moves to the next layer for further optimization. This process continues until the bottom layer is reached; the alignment result at the bottom layer is the alignment parameter.

[0115] Based on the alignment parameters obtained in the previous step, the target image is transformed using these parameters. During the transformation, pixel positions in the target image may map to non-integer positions in the new image. In this case, interpolation methods (such as nearest neighbor interpolation, bilinear interpolation, bicubic interpolation, etc.) can be used to estimate the pixel values ​​at these positions. In the transformed image, some areas may not correspond to any pixels in the original image. In such cases, you can choose to fill them with a specific color (such as black or white) or fill them with the values ​​of surrounding pixels. Thus, the transformed target image is obtained.

[0116] The transformed target image is fused with the initial image of the current frame to obtain the second intermediate image. The image fusion algorithm can be implemented using various existing methods, such as averaging or weighted methods.

[0117] For the averaging method, the corresponding pixel values ​​of the target image and the initial image of the current frame are averaged to obtain the second intermediate image.

[0118] For the weighted method, the corresponding pixel values ​​of the target image and the initial image of the current frame are weighted and averaged to obtain the second intermediate image.

[0119] It should be understood that, in addition to the fusion algorithms listed above, fusion can also be achieved through transform domain-based methods, multi-resolution analysis-based methods, or deep learning-based methods.

[0120] Therefore, the content of the initial image of the current frame can be restored using the above method.

[0121] Repeat the above steps, processing one initial image at a time, until all initial images have been processed.

[0122] Step S230: Obtain the target brightness value of each initial image.

[0123] In this embodiment, for the first initial image, a pre-set target brightness value is obtained, or the target brightness value can be determined based on the initial image taken after the exposure parameters have converged. When determining the target brightness value based on the initial image taken after the exposure parameters have converged, a random selection is made from the initial images taken after the exposure parameters have converged (for example, the first initial image taken after the exposure parameters have converged), and the brightness value corresponding to that initial image is calculated (using the brightness value calculation method in step S1132 above). This brightness value is then determined as the target brightness value.

[0124] For the second initial image to the last initial image before the exposure parameters converge, obtain the brightness value of each initial image, and filter the brightness value of each initial image to obtain the target brightness value of each initial image.

[0125] In this process, the brightness values ​​of each initial image are filtered to obtain the target brightness value of each initial image. Inertial filtering, Kalman filtering, or Wiener filtering can be used.

[0126] Taking Kalman filtering as an example, after obtaining the brightness values ​​of each initial image, a Kalman filter is created to observe the changes in these brightness values. The Kalman filter is updated frame by frame, and the following operations are repeated for each frame: based on the state and motion trend of the previous frame, the predicted value of the current frame is predicted; the actual value is extracted from the affine transformation of the current frame; the Kalman filter automatically weighs the predicted value and the actual value, and outputs a more reliable and smoother estimate to obtain the target brightness value.

[0127] Step S240: Perform brightness processing on the second intermediate image according to the target brightness value to obtain a video frame.

[0128] In this embodiment, the second intermediate image is processed according to the target brightness value to obtain a video frame. The brightness processing can be implemented using various existing methods, and this embodiment of the invention does not limit this approach. For example, the Gamma method can be used for brightness processing. Gamma correction is a non-linear brightness adjustment method that adjusts brightness by exponentially transforming the image pixel values. This method better matches the human eye's perception of brightness and can maintain the overall contrast and detail of the image.

[0129] Specifically, firstly, the actual brightness value of the second intermediate image is calculated (which can be calculated using the brightness value calculation method in step S1132 above), denoted as L. C The target brightness value obtained above is denoted as L. T Based on the actual brightness value L C and target brightness value L T The specific formula for calculating the Gamma value is as follows: Then, the pixel values ​​in the second intermediate image are normalized to the range of 0 to 1, and an exponential operation is performed on each pixel value, with the calculation formula as follows: From this, video frames can be obtained.

[0130] Step S250: Generate the captured video based on the video frames.

[0131] In this embodiment, the obtained video frames are stored sequentially as an image sequence, and the image sequence is synthesized into a video.

[0132] Furthermore, Figure 10 This is a timing diagram of video capture according to an embodiment of the present invention. For example... Figure 10 As shown: At time t0, after receiving the user's shooting command, the image acquisition unit is initialized. Specifically, the image acquisition module is initialized according to the exposure parameters used in the previous shooting, that is, the exposure parameters of the image acquisition module are initialized to the exposure parameters used in the previous shooting.

[0133] Initialization is completed at time t1, that is, the initialization phase is between t0 and t1.

[0134] After initialization, at time t1, the first initial image F1 is captured based on the initialized exposure parameters. Then, the exposure parameters are adjusted based on the first initial image F1, and the next image is captured based on the adjusted exposure parameters. The exposure parameters are adjusted once after each image is captured.

[0135] At time t2, after capturing the Mth frame, exposure parameter convergence is detected. The period between t1 and t2 is the AE convergence phase.

[0136] In video shooting scenarios, after t2, continue shooting the initial image according to the converged exposure parameters, and perform normal shooting within the t2-t3 time period.

[0137] At time t3, after receiving the end command, the recording ends.

[0138] In summary, upon camera startup, the system initializes using the exposure parameters from the previous shot and immediately begins recording. This ensures that no moment the user wants to capture is missed. Simultaneously, low-exposure and high-exposure frames are shot after the first frame to compensate for insufficient information in the first frame. Furthermore, recording can begin immediately without waiting for After Effects (AE) convergence, and AE convergence is gradually achieved during recording. Algorithms smooth brightness and compensate for lost details, noise, and dynamic range using multi-frame information. Post-processing completes AWB (Auto White Balance) smoothing to obtain the captured photos or videos. When recording video, recording also begins immediately upon camera startup. During the gradual exposure convergence, an ideal brightness smoothing target is found. This target is used to adjust the brightness of all unstable frames, and insufficient information in each frame is compensated using preceding and following frames, allowing the user to shoot whenever they want.

[0139] This invention, upon receiving a shooting command, activates an image acquisition module to acquire a series of initial images. For a photo shooting command, the first initial image without exposure extremes is selected as a reference image. A predetermined number of adjacent initial images are selected as target images after the reference image. A noise-reduced first intermediate image is obtained based on the target image and the reference image. The target brightness value is determined, and brightness processing is applied to the first intermediate image based on the target brightness value to obtain the captured photo. Therefore, by using the first initial image without exposure extremes as a reference image for photo shooting processing, photo shooting does not require waiting for automatic exposure convergence, reducing shooting delay and improving shooting timeliness. Simultaneously, no additional components are needed, reducing equipment costs.

[0140] Figure 11 This is a schematic diagram of the imaging device according to an embodiment of the present invention. Figure 11 As shown, the shooting device of this embodiment includes an instruction receiving unit 111, a reference image determining unit 112, a target image determining unit 113, a first intermediate image acquiring unit 114, a target brightness value determining unit 115, and a photo generating unit 116. The instruction receiving unit 111 is used to start the image acquisition module to acquire consecutive initial images in response to receiving a shooting instruction. The reference image determining unit 112 is used to select the first initial image without exposure extremes as a reference image in response to the shooting instruction being a photo shooting instruction. The target image determining unit 113 is used to select a predetermined number of adjacent initial images after the reference image as target images. The first intermediate image acquiring unit 114 is used to acquire a noise-reduced first intermediate image based on the target image and the reference image. The target brightness value determining unit 115 is used to determine a target brightness value. The photo generating unit 116 is used to perform brightness processing on the first intermediate image based on the target brightness value to obtain a captured photo.

[0141] This invention, upon receiving a shooting command, activates an image acquisition module to acquire a series of initial images. For a photo shooting command, the first initial image without exposure extremes is selected as a reference image. A predetermined number of adjacent initial images are selected as target images after the reference image. A noise-reduced first intermediate image is obtained based on the target image and the reference image. The target brightness value is determined, and brightness processing is applied to the first intermediate image based on the target brightness value to obtain the captured photo. Therefore, by using the first initial image without exposure extremes as a reference image for photo shooting processing, photo shooting does not require waiting for automatic exposure convergence, reducing shooting delay and improving shooting timeliness. Simultaneously, no additional components are needed, reducing equipment costs.

[0142] Specifically, Figure 1 The imaging device shown includes: one or more processors 11 and memory 12. Figure 1 Taking a processor 11 as an example, the processor 11 and the memory 12 can be connected via a bus or other means. Figure 1 Taking a bus connection as an example, memory 12, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Processor 11 executes various functional applications and data processing of the device by running the non-volatile software programs, instructions, and modules stored in memory 12, thereby realizing the above-mentioned shooting method.

[0143] The memory 12 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store an option list, etc. Furthermore, the memory 12 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 12 may optionally include memory remotely located relative to the processor 11, and these remote memories can be connected to external devices via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0144] One or more modules are stored in memory 12 and, when executed by one or more processors 11, perform the shooting method in any of the above method embodiments.

[0145] The above-mentioned products can perform the methods provided in the embodiments of this application, and have the corresponding functional modules and beneficial effects of performing the methods. For technical details not described in detail in this embodiment, please refer to the methods provided in the embodiments of this application.

[0146] This invention, upon receiving a shooting command, activates an image acquisition module to acquire a series of initial images. For a photo shooting command, the first initial image without exposure extremes is selected as a reference image. A predetermined number of adjacent initial images are selected as target images after the reference image. A noise-reduced first intermediate image is obtained based on the target image and the reference image. The target brightness value is determined, and brightness processing is applied to the first intermediate image based on the target brightness value to obtain the captured photo. Therefore, by using the first initial image without exposure extremes as a reference image for photo shooting processing, photo shooting does not require waiting for automatic exposure convergence, reducing shooting delay and improving shooting timeliness. Simultaneously, no additional components are needed, reducing equipment costs.

[0147] Another embodiment of the present invention relates to a non-volatile storage medium for storing a computer-readable program for use by a computer to execute some or all of the above-described method embodiments.

[0148] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0149] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A shooting method, characterized in that, The method includes: In response to receiving a shooting command, the image acquisition module is activated to acquire a series of initial images; In response to the shooting instruction being a photo shooting instruction, the first initial image without exposure extremes is selected as the reference image from the initial images; A predetermined number of adjacent initial images are selected as target images after the reference image; A first intermediate image after noise reduction is obtained based on the target image and the reference image; Determine the target brightness value; The first intermediate image is processed for brightness based on the target brightness value to obtain the captured photograph.

2. The method according to claim 1, characterized in that, The step of activating the image acquisition module to acquire continuous initial images in response to receiving a shooting command includes: In response to receiving a shooting command, the exposure parameters used by the image acquisition module in the previous shooting are obtained; The image acquisition module is initialized based on the exposure parameters used in the previous capture. After initialization, the image acquisition module acquires a series of initial images.

3. The method according to claim 2, characterized in that, After initialization, acquiring continuous initial images via the image acquisition module includes: The first frame of the initial image is captured based on the initialized exposure parameters; Adjust the exposure parameters based on the first initial image frame, and capture the next initial image frame based on the adjusted exposure parameters. Repeat the exposure parameter adjustment and image capture process until the exposure parameters converge. After the exposure parameters converge, the initial image is captured based on the converged exposure parameters.

4. The method according to claim 3, characterized in that, The step of adjusting the exposure parameters based on the first frame initial image includes: In response to the shooting command being a photo shooting command, the exposure parameters are adjusted through a first exposure algorithm, wherein the adjustment parameters of the first exposure algorithm are set to high-speed mode to accelerate the convergence speed; In response to the shooting command being a video shooting command, the exposure parameters are adjusted through the second exposure algorithm, and the adjustment parameters of the second exposure algorithm are set to normal mode.

5. The method according to claim 1, characterized in that, The reference image is obtained by iteratively performing the following steps: Select one initial image as a candidate image according to the order of the initial images; Determine the exposure score corresponding to the candidate image; In response to the exposure score being greater than or equal to a first threshold and less than or equal to a second threshold, the candidate image is determined as a reference image; In response to the exposure score being less than a first threshold or the exposure score being greater than a second threshold, the initial image of the next frame is selected as a candidate image.

6. The method according to claim 1, characterized in that, The step of obtaining the first intermediate image after noise reduction based on the target image and the reference image includes: Calculate the alignment parameters between each target image and the reference image; The target image is transformed according to the alignment parameters; The transformed target image is fused with the reference image to obtain the first intermediate image.

7. The method according to claim 3, characterized in that, Determining the target brightness value includes: Get the preset target brightness value; or The target brightness value is determined based on the initial image taken after the exposure parameters converge.

8. The method according to claim 3, characterized in that, The method further includes: In response to the shooting command being a video shooting command, noise reduction processing is performed on each initial image to obtain a second intermediate image; Obtain the target brightness values ​​for each initial image; The second intermediate image is processed for brightness based on the target brightness value to obtain a video frame; The video is generated based on the video frames.

9. The method according to claim 8, characterized in that, The step of performing noise reduction processing on each initial image to obtain the second intermediate image includes: Select the current frame initial image to be processed from the initial images in sequence; A predetermined number of adjacent initial images are selected as target images after the initial image of the current frame; The second intermediate image after noise reduction is obtained based on the initial image of the current frame and the target image.

10. The method according to claim 8, characterized in that, The process of obtaining the target brightness value of each initial image includes: For the first initial image, obtain the preset target brightness value, or determine the target brightness value based on the initial image taken after the exposure parameters converge; For the second initial image to the last initial image before the exposure parameters converge, obtain the brightness value of each initial image, and filter the brightness value of each initial image to obtain the target brightness value of each initial image.

11. A shooting device, characterized in that, The device includes: The instruction receiving unit is used to start the image acquisition module to acquire continuous initial images in response to receiving a shooting instruction; A reference image determination unit is configured to select the first initial image without exposure extremes as a reference image in response to the shooting instruction being a photo shooting instruction; The target image determination unit is used to select a predetermined number of adjacent initial images as target images after the reference image; The first intermediate image acquisition unit is used to acquire a denoised first intermediate image based on the target image and the reference image; The target brightness value determination unit is used to determine the target brightness value; The photo generation unit is used to perform brightness processing on the first intermediate image according to the target brightness value to obtain the captured photo.

12. A shooting device, comprising an image acquisition module, a memory, and a processor, characterized in that, The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method as described in any one of claims 1-10.

13. A computer-readable storage medium storing computer program instructions thereon, characterized in that, The computer program instructions, when executed by a processor, implement the method as described in any one of claims 1-10.

14. A computer program product comprising a computer program, characterized in that, When the computer program is run on a computer, the computer performs the method according to any one of claims 1-10.

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