Exposure adjustment method, device and storage medium

Abstract

The present application discloses an exposure adjustment method, a device and a storage medium. The exposure adjustment method comprises: using a current exposure ratio to perform image acquisition to obtain a current imaging picture; calculating a signal-to-noise ratio corresponding to the current imaging picture to obtain a signal-to-noise ratio corresponding to the current exposure ratio; and obtaining a plurality of exposure ratios that are close to the current exposure ratio value to obtain a plurality of candidate exposure ratios, predicting the signal-to-noise ratio of the current imaging picture at each candidate exposure ratio, and respectively obtaining a signal-to-noise ratio corresponding to each candidate exposure ratio; selecting an exposure ratio corresponding to a maximum signal-to-noise ratio from the signal-to-noise ratio corresponding to the current exposure ratio and the signal-to-noise ratio corresponding to each candidate exposure ratio to obtain a target exposure ratio; performing subsequent image acquisition based on the target exposure ratio, and selecting a final exposure ratio by traversing the signal-to-noise ratios corresponding to various exposure ratios, so as to not easily fall into a local optimal solution, quickly perform exposure adjustment, and improve the signal-to-noise ratio of the adjusted imaging picture.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an exposure adjustment method, device and storage medium. Background Art

[0002] Traditional wide dynamic technology usually uses a fixed exposure ratio to adjust the exposure of long and short frames. However, this one-size-fits-all approach is often difficult to take into account when facing high dynamic range and low dynamic range scenes, resulting in improper exposure ratio settings and reduced image quality of the final composite image. Specifically, if a high exposure ratio is used in a low dynamic range scene, the dark details in the short frame will be over-fused into the composite frame, causing significant noise problems; conversely, if a low exposure ratio is used in a high dynamic range scene, the signal-to-noise ratio of the long frame in the dark area is not much improved compared to the short frame, and when the dark area is enhanced, it is also easy to generate noise.

[0003] Therefore, how to accurately and effectively adjust the exposure ratio used for image acquisition is a technical problem that needs to be urgently solved by those skilled in the art. Summary of the invention

[0004] In order to solve the above technical problems, the present application at least provides an exposure adjustment method, device and storage medium.

[0005] In a first aspect, the present application provides an exposure adjustment method, the method comprising: using a current exposure ratio to perform image acquisition to obtain a current imaging picture, the current imaging picture being synthesized by a long-frame image and a short-frame image, and the current exposure ratio refers to the ratio between the maximum brightness of the long-frame image and the maximum brightness of the short-frame image; calculating the signal-to-noise ratio corresponding to the current imaging picture to obtain the signal-to-noise ratio corresponding to the current exposure ratio; and obtaining a plurality of exposure ratios close to the current exposure ratio value to obtain a plurality of candidate exposure ratios, predicting the signal-to-noise ratio of the current imaging picture at each candidate exposure ratio, and respectively obtaining the signal-to-noise ratio corresponding to each candidate exposure ratio; selecting the exposure ratio corresponding to the maximum signal-to-noise ratio from the signal-to-noise ratio corresponding to the current exposure ratio and the signal-to-noise ratio corresponding to each candidate exposure ratio to obtain the target exposure ratio; and performing subsequent image acquisition based on the target exposure ratio.

[0006] In one embodiment, the method also includes: obtaining a brightness signal-to-noise ratio function corresponding to the current exposure ratio and each candidate exposure ratio respectively; wherein the brightness signal-to-noise ratio function is used to describe the signal-to-noise ratio corresponding to different pixel brightnesses; taking a short-frame image or a long-frame image corresponding to the current imaging picture as a reference image, obtaining a histogram of the reference image, and obtaining a reference histogram; wherein the reference histogram is used to describe the number of pixel points corresponding to different pixel brightnesses in the reference image; calculating the signal-to-noise ratio corresponding to the current imaging picture to obtain the signal-to-noise ratio corresponding to the current exposure ratio, including: multiplying the brightness signal-to-noise ratio function corresponding to the current exposure ratio and the value corresponding to each pixel brightness of the reference histogram, and accumulating the product of each pixel brightness to obtain the signal-to-noise ratio corresponding to the current exposure ratio; predicting the signal-to-noise ratio of the current imaging picture at each candidate exposure ratio to obtain the signal-to-noise ratio corresponding to each candidate exposure ratio, including: multiplying the brightness signal-to-noise ratio function corresponding to the candidate exposure ratio and the value corresponding to each pixel brightness of the reference histogram, and accumulating the product of each pixel brightness to obtain the signal-to-noise ratio corresponding to the candidate exposure ratio.

[0007] In one embodiment, the brightness signal-to-noise ratio function corresponding to the current exposure ratio and each candidate exposure ratio is obtained, including: based on the fact that image brightness is proportional to the signal-to-noise ratio, constructing the brightness signal-to-noise ratio function of the long frame image and the brightness signal-to-noise ratio function of the short frame image corresponding to each exposure ratio, and obtaining the long frame signal-to-noise ratio function and the short frame signal-to-noise ratio function corresponding to each exposure ratio; wherein the product between the slope of the long frame signal-to-noise ratio function and the slope of the short frame signal-to-noise ratio function corresponding to any exposure ratio is equal to any exposure ratio; for the long frame signal-to-noise ratio function and the short frame signal-to-noise ratio function corresponding to each exposure ratio, dividing the image brightness range from small to large into a long frame range, a fusion range and a short frame range, fusing the long frame signal-to-noise ratio function and the short frame signal-to-noise ratio function in the fusion range to obtain a fusion function corresponding to the fusion range; and sequentially splicing the long frame signal-to-noise ratio function in the long frame range, the fusion function in the fusion range and the short frame signal-to-noise ratio function in the short frame range to obtain the brightness signal-to-noise ratio function corresponding to each exposure ratio.

[0008] In one embodiment, the image brightness range is divided into a long frame range, a fusion range and a short frame range in order from small to large, including: taking a preset brightness value range in the image brightness range corresponding to the long frame image as the fusion range, taking a brightness range corresponding to a minimum brightness value less than the preset brightness value range as the long frame range, and taking a brightness range corresponding to a maximum brightness value greater than the preset brightness value range as the short frame range.

[0009] In one embodiment, the method also includes: in response to a change in the exposure time of a benchmark image corresponding to any candidate exposure ratio relative to the exposure time of a benchmark image corresponding to a current exposure ratio, calculating the ratio between the exposure time of the benchmark image corresponding to the candidate exposure ratio and the exposure time of the benchmark image corresponding to the current exposure ratio to obtain a signal-to-noise ratio exposure time coefficient; multiplying the brightness signal-to-noise ratio function corresponding to the candidate exposure ratio and the value corresponding to the brightness of each pixel of the benchmark histogram, and accumulating the product of the brightness of each pixel to obtain the signal-to-noise ratio corresponding to the candidate exposure ratio, including: multiplying the brightness signal-to-noise ratio function corresponding to the candidate exposure ratio and the value corresponding to the brightness of each pixel of the benchmark histogram, and accumulating the product of the brightness of each pixel to obtain an initial sum; and calculating the product of the initial sum and the signal-to-noise ratio exposure time coefficient to obtain the signal-to-noise ratio corresponding to the candidate exposure ratio.

[0010] In one embodiment, subsequent image acquisition is performed based on the target exposure ratio, including: selecting an exposure ratio value from the middle of a numerical range from the current exposure ratio to the target exposure ratio to obtain a gradient adjustment value; performing subsequent image acquisition based on the gradient adjustment value, and calculating a new target exposure ratio corresponding to an imaging screen acquired by the gradient adjustment value; performing subsequent image acquisition based on the new target exposure ratio corresponding to the gradient adjustment value, until the calculated new target exposure ratio is equal to the gradient adjustment value used for image acquisition, thereby completing exposure adjustment.

[0011] In one embodiment, the number of gradient adjustment values ​​is multiple, and the method further includes: if the new target exposure ratios calculated by accumulating a preset number of gradient adjustment values ​​are the same, taking the new target exposure ratios corresponding to the preset number of gradient adjustment values ​​as the final adjustment value; and using the final adjustment value for subsequent image acquisition.

[0012] In one embodiment, performing subsequent image acquisition based on the target exposure ratio includes: directly using the target exposure ratio to perform subsequent image acquisition.

[0013] According to a second aspect of the present application, there is provided an exposure adjustment device, which includes: a current picture acquisition module, which is used to acquire an image using a current exposure ratio to obtain a current imaging picture, wherein the current imaging picture is synthesized by a long-frame image and a short-frame image, and the current exposure ratio refers to the ratio between the maximum brightness of the long-frame image and the maximum brightness of the short-frame image; a signal-to-noise ratio calculation module, which is used to calculate the signal-to-noise ratio corresponding to the current imaging picture, and obtain the signal-to-noise ratio corresponding to the current exposure ratio; and obtain a plurality of exposure ratios close to the current exposure ratio value to obtain a plurality of candidate exposure ratios, predict the signal-to-noise ratio of the current imaging picture at each candidate exposure ratio, and obtain the signal-to-noise ratio corresponding to each candidate exposure ratio; an exposure ratio selection module, which is used to select the exposure ratio corresponding to the maximum signal-to-noise ratio from the signal-to-noise ratio corresponding to the current exposure ratio and the signal-to-noise ratio corresponding to each candidate exposure ratio, and obtain the target exposure ratio; and an exposure ratio adjustment module, which is used to perform subsequent image acquisition based on the target exposure ratio.

[0014] A third aspect of the present application provides an electronic device, including a memory and a processor, wherein the processor is used to execute program instructions stored in the memory to implement the above-mentioned exposure adjustment method.

[0015] A fourth aspect of the present application provides a computer-readable storage medium on which program instructions are stored, and when the program instructions are executed by a processor, the above-mentioned exposure adjustment method is implemented.

[0016] The above scheme obtains the current imaging picture by using the current exposure ratio for image acquisition, the current imaging picture is synthesized by the long-frame image and the short-frame image, and the current exposure ratio refers to the ratio between the maximum brightness of the long-frame image and the maximum brightness of the short-frame image; calculates the signal-to-noise ratio corresponding to the current imaging picture to obtain the signal-to-noise ratio corresponding to the current exposure ratio; and obtains multiple exposure ratios close to the current exposure ratio value to obtain multiple candidate exposure ratios, predicts the signal-to-noise ratio of the current imaging picture under each candidate exposure ratio, and obtains the signal-to-noise ratio corresponding to each candidate exposure ratio; selects the exposure ratio corresponding to the maximum signal-to-noise ratio from the signal-to-noise ratio corresponding to the current exposure ratio and the signal-to-noise ratio corresponding to each candidate exposure ratio to obtain the target exposure ratio; performs subsequent image acquisition based on the target exposure ratio, and selects the final exposure ratio by traversing the signal-to-noise ratios corresponding to various exposure ratios, which is not easy to fall into the local optimal solution, quickly adjusts the exposure, and improves the signal-to-noise ratio of the adjusted imaging picture.

[0017] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present application and are used together with the specification to illustrate the technical solution of the present application.

[0019] Figure 1 is a schematic diagram of a solution implementation environment shown in an exemplary embodiment of the present application;

[0020] Figure 2 is a flow chart of an exposure adjustment method shown in an exemplary embodiment of the present application;

[0021] Figure 3 is a schematic diagram of a long frame signal-to-noise ratio function and a short frame signal-to-noise ratio function shown in an exemplary embodiment of the present application;

[0022] Figure 4 is a schematic diagram of fusing a long frame signal-to-noise ratio function and a short frame signal-to-noise ratio function according to an exemplary embodiment of the present application;

[0023] Figure 5 is a schematic diagram showing a change in exposure time of a reference image according to an exemplary embodiment of the present application;

[0024] Figure 6 is a schematic diagram of calculating a signal-to-noise ratio according to an exemplary embodiment of the present application;

[0025] Figure 7 is a schematic diagram of signal-to-noise ratios corresponding to various exposure ratios shown in an exemplary embodiment of the present application;

[0026] Figure 8 is a block diagram of an exposure adjustment device shown in an exemplary embodiment of the present application;

[0027] Fig. 9 is a schematic structural diagram of an electronic device shown in an exemplary embodiment of the present application;

[0028] Fig.10 It is a schematic diagram of the structure of a computer-readable storage medium shown in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0029] The scheme of the embodiment of the present application is described in detail below in conjunction with the drawings of the specification.

[0030] In the following description, for the purpose of explanation rather than limitation, specific details such as specific system structures, interfaces, and technologies are provided to facilitate a thorough understanding of the present application.

[0031] The term "and / or" in this article is only an association information describing the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the previous and next associated objects are in an "or" relationship. In addition, "many" in this article means two or more than two. In addition, the term "at least one" in this article means any combination of at least two of any one or more of a plurality of, for example, including at least one of A, B, and C, can mean including any one or more elements selected from the set consisting of A, B, and C.

[0032] The exposure adjustment method provided in the embodiment of the present application is described below.

[0033] Please refer to Figure 1 , Figure 1 1 is a schematic diagram of a solution implementation environment shown in an exemplary embodiment of the present application. The solution implementation environment may include an image acquisition device 110 and a server 120, and the image acquisition device 110 and the server 120 are connected to each other for communication.

[0034] The image acquisition device 110 is used for performing image acquisition.

[0035] Server 120 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), as well as big data and artificial intelligence platforms.

[0036] In one example, an exposure adjustment program is installed and run inside the image acquisition device 110, and the exposure adjustment program is used to adjust the exposure ratio of the image acquisition device 110 according to the image acquired by the image acquisition device 110. The server 120 can be a background server of the exposure adjustment program, which is used to provide background services for the exposure adjustment program.

[0037] In one example, the server 120 may analyze the image acquired from the image acquisition device 110 at the current exposure ratio to obtain a target exposure ratio. The server 120 may send the target exposure ratio to the image acquisition device 110, and the image acquisition device 110 may perform subsequent image acquisition based on the target exposure ratio.

[0038] In the exposure adjustment method provided in the embodiment of the present application, the execution subject of each step may be the image acquisition device 110, or the server 120, or the image acquisition device 110 and the server 120 may cooperate with each other to execute the method, that is, part of the steps of the method are executed by the image acquisition device 110 and the other part of the steps are executed by the server 120.

[0039] See also Figure 2 , Figure 2 is a flowchart of an exposure adjustment method shown in an exemplary embodiment of the present application. The exposure adjustment method can be applied to Figure 1 It should be understood that the method can also be applied to other exemplary implementation environments and be specifically executed by devices in other implementation environments, and this embodiment does not limit the implementation environment to which the method is applicable.

[0040] like Figure 2 As shown, the exposure adjustment method at least includes steps S210 to S240, which are described in detail as follows:

[0041] Step S210: using the current exposure ratio to perform image acquisition to obtain a current imaging picture, the current imaging picture is synthesized by a long frame image and a short frame image, and the current exposure ratio refers to the ratio between the maximum brightness of the long frame image and the maximum brightness of the short frame image.

[0042] The exposure ratio of the current imaging picture is the ratio of the exposure amounts of the long-frame image and the short-frame image that synthesize the current imaging picture, that is, the ratio between the maximum brightness of the long-frame image and the maximum brightness of the short-frame image. It can also be regarded as the ratio between the exposure time of the long-frame image and the exposure time of the short-frame image that synthesize the current imaging picture.

[0043] The exposure time of long-frame images is longer, which is mainly used to ensure the clarity of details in dark areas and a good signal-to-noise ratio. In dark light environments, long-frame images can capture more detail information, but overexposure may occur in bright areas.

[0044] The exposure time of short-frame images is shorter, and they are mainly used to capture details in overexposed areas of long-frame images. In strong light environments, short-frame images can provide more details in bright areas, but may not perform well in dark areas.

[0045] Step S220: Calculate the signal-to-noise ratio corresponding to the current imaging picture to obtain the signal-to-noise ratio corresponding to the current exposure ratio; and obtain multiple exposure ratios close to the current exposure ratio value to obtain multiple candidate exposure ratios, predict the signal-to-noise ratio of the current imaging picture at each candidate exposure ratio, and obtain the signal-to-noise ratio corresponding to each candidate exposure ratio.

[0046] The signal-to-noise ratio refers to the ratio of signal strength to noise strength. It is a key indicator for measuring the information clarity of an image. A high signal-to-noise ratio means that the image signal is pure and the image clarity is high; a low signal-to-noise ratio means that the image noise interference is large and the image clarity is low.

[0047] Exemplarily, the signal power and noise power corresponding to the current imaging picture can be directly calculated, and then the signal-to-noise ratio corresponding to the current imaging picture is calculated based on the signal power and noise power, and the signal-to-noise ratio corresponding to the current imaging picture is used as the signal-to-noise ratio corresponding to the current exposure ratio.

[0048] Exemplarily, it is assumed that the signal-to-noise ratio is only related to the brightness of the image, and a histogram corresponding to the current imaging picture, long-frame image or short-frame image is obtained. The histogram describes the number of pixels corresponding to different pixel brightnesses, and the signal-to-noise ratio corresponding to the current exposure ratio is calculated based on the histogram and the functional relationship between the signal-to-noise ratio and the brightness.

[0049] And, a plurality of exposure ratios close to the current exposure ratio value are obtained to obtain a plurality of candidate exposure ratios.

[0050] For example, if the current exposure ratio value is x, all integers in the range of [xa, x+b] are set as exposure ratios close to the current exposure ratio value, and multiple candidate exposure ratios are obtained. The values ​​of a and b can be pre-set based on experience, or can be set according to the number of candidate exposure ratios that need to be selected. This application does not limit this.

[0051] For another example, if the current exposure ratio value is x, the selected candidate exposure ratios include: x-6, x-3, x-1, x+1, x+3, x+6, where the specific formula for calculating the candidate exposure ratio can be flexibly set according to the actual application scenario, and this application does not limit this.

[0052] In some embodiments, the number of selected candidate exposure ratios can be pre-set or flexibly calculated. For example, the number of candidate exposure ratios to be selected is calculated based on the signal-to-noise ratio corresponding to the current imaging picture. The higher the signal-to-noise ratio corresponding to the current imaging picture, the smaller the number of candidate exposure ratios. The lower the signal-to-noise ratio corresponding to the current imaging picture, the greater the number of candidate exposure ratios. This allows for quick exposure adjustment while saving computing resources.

[0053] The signal-to-noise ratio of the current imaging picture at each candidate exposure ratio is predicted, and the signal-to-noise ratio corresponding to each candidate exposure ratio is obtained respectively.

[0054] Exemplarily, the signal-to-noise ratio is set to be only related to the brightness of the image. If the current exposure ratio is currExpRa, because the brightest brightness of the long-frame image is exactly 1 / currExpRa of the brightest brightness of the short-frame image, and the exposure time of the long-frame image is currExpRa times the exposure time of the short-frame image, the maximum signal-to-noise ratio of the long-frame image and the short-frame image can be obtained to be consistent. Therefore, the signal-to-noise ratio corresponding to other exposure ratios can be derived based on the signal-to-noise ratio corresponding to the current exposure ratio and the correlation between the brightness and signal-to-noise ratio corresponding to different exposure ratios, thereby estimating the signal-to-noise ratio corresponding to each candidate exposure ratio.

[0055] Exemplarily, a pre-trained neural network model can be used to predict the signal-to-noise ratio under each candidate exposure ratio to obtain the signal-to-noise ratio corresponding to each candidate exposure ratio. The neural network model can be trained using sample images, the sample images are composed of multiple sample pairs, and a sample pair contains multiple images of the same scene captured using different exposure ratios. An image captured under one exposure ratio in the sample pair is input into the pre-trained neural network model, and the predicted signal-to-noise ratio of the image captured under other exposure ratios is input through the neural network model. According to the predicted signal-to-noise ratio and the actual signal-to-noise ratio of the image, the network parameters of the pre-trained neural network model are adjusted to obtain the final trained neural network model.

[0056] Of course, other methods may also be used to predict the signal-to-noise ratio of the current imaging picture at each candidate exposure ratio, and this application does not limit this.

[0057] Step S230: Selecting the exposure ratio corresponding to the maximum signal-to-noise ratio from the signal-to-noise ratio corresponding to the current exposure ratio and the signal-to-noise ratio corresponding to each candidate exposure ratio to obtain the target exposure ratio.

[0058] By traversing the signal-to-noise ratio corresponding to each candidate exposure ratio and directly selecting the exposure ratio corresponding to the maximum signal-to-noise ratio as the target exposure ratio, the exposure adjustment can be performed quickly, and a new exposure ratio can be selected with the goal of improving the signal-to-noise ratio, thereby improving the image quality of the acquired image.

[0059] Step S240: performing subsequent image acquisition based on the target exposure ratio.

[0060] The target exposure ratio may be directly used for subsequent image acquisition; or the target exposure ratio may be used as an adjustment direction to gradually adjust the exposure ratio of each image acquisition.

[0061] Next, some embodiments of calculating the signal-to-noise ratio corresponding to each exposure ratio are described in detail.

[0062] In some embodiments, a brightness signal-to-noise ratio function corresponding to the current exposure ratio and each candidate exposure ratio is obtained, and the brightness signal-to-noise ratio function is used to describe the signal-to-noise ratio corresponding to different pixel brightnesses; and a short-frame image or a long-frame image corresponding to the current imaging screen is used as a reference image, and a histogram of the reference image is obtained to obtain a reference histogram, which is used to describe the number of pixel points corresponding to different pixel brightnesses in the reference image.

[0063] Calculating the signal-to-noise ratio corresponding to the current imaging picture to obtain the signal-to-noise ratio corresponding to the current exposure ratio includes: multiplying the brightness signal-to-noise ratio function corresponding to the current exposure ratio and the value corresponding to the brightness of each pixel of the reference histogram, and accumulating the product of the brightness of each pixel to obtain the signal-to-noise ratio corresponding to the current exposure ratio.

[0064] Predict the signal-to-noise ratio of the current imaging picture at each candidate exposure ratio, and obtain the signal-to-noise ratio corresponding to each candidate exposure ratio, including: multiplying the brightness signal-to-noise ratio function corresponding to the candidate exposure ratio and the value corresponding to each pixel brightness of the reference histogram, and accumulating the product of each pixel brightness to obtain the signal-to-noise ratio corresponding to the candidate exposure ratio.

[0065] The brightness signal-to-noise ratio function may be constructed by analyzing a large number of images with different exposure ratios, or may be constructed by constructing functions for different exposure ratios based on the correlation between brightness and signal-to-noise ratio.

[0066] Taking the construction of the brightness signal-to-noise ratio function according to the correlation between brightness and signal-to-noise ratio as an example, the construction steps of the brightness signal-to-noise ratio function are illustrated as follows:

[0067] In some embodiments, based on the fact that image brightness is proportional to signal-to-noise ratio, brightness signal-to-noise ratio functions of long-frame images and brightness signal-to-noise ratio functions of short-frame images corresponding to respective exposure ratios are constructed, and long-frame signal-to-noise ratio functions and short-frame signal-to-noise ratio functions corresponding to respective exposure ratios are obtained; wherein the product of the slope of the long-frame signal-to-noise ratio function and the slope of the short-frame signal-to-noise ratio function corresponding to any exposure ratio is equal to any exposure ratio.

[0068] Optionally, for ease of calculation, the slope of the brightness signal-to-noise ratio function corresponding to the benchmark image is set to 1, and the benchmark image is used as the basis for calculating the candidate exposure ratio. After modifying the exposure ratio, the exposure time of the benchmark image is kept unchanged as much as possible, that is, the slope of the brightness signal-to-noise ratio function corresponding to the benchmark image is kept at 1.

[0069] Taking the reference image as a short-frame image as an example, the short-frame signal-to-noise ratio function is specifically a directly proportional function with a slope of 1. The product of the slope of the long-frame signal-to-noise ratio function corresponding to any exposure ratio and the slope of the short-frame signal-to-noise ratio function is equal to any exposure ratio. The long-frame signal-to-noise ratio function is specifically a directly proportional function with a slope of the exposure ratio.

[0070] For example, taking the current exposure ratio equal to 4, please refer to Figure 3 , Figure 3 This is a schematic diagram of a long frame signal-to-noise ratio function and a short frame signal-to-noise ratio function shown in an exemplary embodiment of the present application. Figure 3 As shown, the slope of the short frame signal-to-noise ratio function is 1, and the slope of the long frame signal-to-noise ratio function is the exposure ratio, that is, the slope of the long frame signal-to-noise ratio function is 4.

[0071] Then, for the long frame signal-to-noise ratio function and the short frame signal-to-noise ratio function corresponding to each exposure ratio, the image brightness range is divided into a long frame range, a fusion range and a short frame range from small to large, and the long frame signal-to-noise ratio function and the short frame signal-to-noise ratio function in the fusion range are fused to obtain the fusion function corresponding to the fusion range; the long frame signal-to-noise ratio function in the long frame range, the fusion function in the fusion range and the short frame signal-to-noise ratio function in the short frame range are spliced ​​in turn to obtain the brightness signal-to-noise ratio function corresponding to each exposure ratio.

[0072] Among them, the methods of fusing the long frame signal-to-noise ratio function and the short frame signal-to-noise ratio function within the fusion range include linear interpolation fusion, parabola interpolation fusion, and the like.

[0073] Exemplarily, a preset brightness value range in the image brightness range corresponding to the long frame image is used as the fusion range, a brightness range corresponding to a minimum brightness value less than the preset brightness value range is used as the long frame range, and a brightness range corresponding to a maximum brightness value greater than the preset brightness value range is used as the short frame range.

[0074] Of course, the fusion range, long frame range and short frame range can also be divided based on the image brightness range corresponding to the short frame image, and this application does not limit this.

[0075] For example, take the current exposure ratio equal to 4, the 75% to 98% brightness range corresponding to the long frame image as the fusion range, the brightness range less than 75% corresponding to the long frame image as the long frame range, and the brightness range greater than 98% corresponding to the long frame image as the short frame range. Figure 4 , Figure 4 FIG. 1 is a schematic diagram showing a fusion of a long frame signal-to-noise ratio function and a short frame signal-to-noise ratio function according to an exemplary embodiment of the present application. Figure 4 As shown, the image brightness range is divided into a long frame range, a fusion range and a short frame range from small to large, and the long frame signal-to-noise ratio function and the short frame signal-to-noise ratio function in the fusion range are linearly interpolated and fused to obtain the fusion function corresponding to the fusion range; then, the long frame signal-to-noise ratio function in the long frame range is retained, and the short frame signal-to-noise ratio function in the short frame range is retained, and finally the brightness signal-to-noise ratio function corresponding to the current exposure ratio is obtained by splicing.

[0076] Similarly, the brightness signal-to-noise ratio function corresponding to each candidate exposure ratio can be obtained through the above method.

[0077] The above embodiment mentions that for the convenience of calculation, the reference image is used as the basis when calculating the candidate exposure ratio. After the exposure ratio is modified, the exposure time of the reference image is kept unchanged as much as possible. However, there is still a situation where the shutter of the reference image is restricted, resulting in a change in the exposure time of the reference image. For this situation, it is necessary to normalize the signal-to-noise ratio corresponding to the exposure ratio finally calculated. The specific steps include:

[0078] In response to a change in the exposure time of a benchmark image corresponding to any candidate exposure ratio relative to the exposure time of a benchmark image corresponding to a current exposure ratio, a ratio between the exposure time of the benchmark image corresponding to the candidate exposure ratio and the exposure time of the benchmark image corresponding to the current exposure ratio is calculated to obtain a signal-to-noise ratio exposure time coefficient; a brightness signal-to-noise ratio function corresponding to the candidate exposure ratio and a value corresponding to the brightness of each pixel of a benchmark histogram are multiplied, and the product of the brightness of each pixel is accumulated to obtain the signal-to-noise ratio corresponding to the candidate exposure ratio, including: multiplying the brightness signal-to-noise ratio function corresponding to the candidate exposure ratio and a value corresponding to the brightness of each pixel of the benchmark histogram, and accumulating the product of the brightness of each pixel to obtain an initial sum; and multiplying the initial sum and the signal-to-noise ratio exposure time coefficient to obtain the signal-to-noise ratio corresponding to the candidate exposure ratio.

[0079] For an example, see Figure 5 , Figure 5 FIG. 1 is a schematic diagram showing a change in exposure time of a reference image according to an exemplary embodiment of the present application. Figure 5 As shown in Figure 2, taking the short frame image as the reference image, the current exposure ratio of 4, and the candidate exposure ratio of 8 as an example, the brightness signal-to-noise ratio function corresponding to the current exposure ratio of 4 (red curve) and the brightness signal-to-noise ratio function corresponding to the candidate exposure ratio of 8 (green curve) are as follows: Figure 5 As shown in the figure, if the upper limit of the exposure time is recorded as maxShut, and the value of maxShut is 40ms as an example, the exposure time of the short frame image changes from 40 / (4+1)=8ms of the 4-fold exposure ratio to 40 / (8+1)=4.44ms of the 8-fold exposure ratio, and the exposure time of the short frame image is reduced by 44.5%.

[0080] The ratio of the exposure time of the benchmark image corresponding to the candidate exposure ratio to the exposure time of the benchmark image corresponding to the current exposure ratio is calculated to obtain the signal-to-noise ratio exposure time coefficient, denoted as snrExpRa. That is, the ratio of 4.44ms of 8 times the exposure ratio to 8ms of 4 times the exposure ratio is calculated, and the signal-to-noise ratio exposure time coefficient snrExpRa is 44.5%.

[0081] Then, the initial sum calculated for the 8x exposure ratio needs to be multiplied by the exposure time factor 44.5% to obtain the final signal-to-noise ratio corresponding to the 8x exposure ratio.

[0082] By calculating the exposure time coefficient in the above manner, the signal-to-noise ratios at different exposure ratios can be normalized so that the signal-to-noise ratios at different exposure ratios are comparable.

[0083] It should be noted that since exposure will involve gain and aperture, even if Figure 5 As shown in , the short frame exposure time is reduced, but because of the adjustment of gain and aperture, the distribution of the histogram of the short frame after final exposure does not change, which once again illustrates the strong correlation between the signal-to-noise ratio and the amount of light entering.

[0084] In summary, after obtaining the brightness signal-to-noise ratio function corresponding to each exposure ratio, the brightness signal-to-noise ratio function and the reference histogram are convolved to obtain the signal-to-noise ratio corresponding to each exposure ratio.

[0085] For example, see Figure 6 , Figure 6 FIG. 1 is a schematic diagram showing a method for calculating a signal-to-noise ratio according to an exemplary embodiment of the present application. Figure 6 As shown, the brightness signal-to-noise ratio function (red curve) and the reference histogram (blue curve) corresponding to the exposure ratio to be calculated (such as the current exposure ratio, each candidate exposure ratio, etc.) are mapped to a coordinate system, the horizontal axis of the coordinate system represents the brightness, and the vertical axis represents the signal-to-noise ratio. The brightness signal-to-noise ratio function and the reference histogram in the coordinate system are directly convolved, and the sum of the calculated signal-to-noise ratios is used as the signal-to-noise ratio corresponding to the exposure ratio to be calculated.

[0086] Specifically, the brightness signal-to-noise ratio function and the value corresponding to each pixel brightness of the reference histogram are multiplied, and then the product of each pixel brightness is accumulated, and the accumulated result is used as the signal-to-noise ratio corresponding to the exposure ratio to be calculated.

[0087] By calculating the signal-to-noise ratio corresponding to each exposure ratio in the above manner, there is no need to worry about parameters such as the gain used in image acquisition. Only the amount of light entering the image needs to be considered. The algorithm is simple and the signal-to-noise ratio can be calculated quickly.

[0088] Then, the exposure ratio corresponding to the maximum signal-to-noise ratio is selected from the signal-to-noise ratio corresponding to the current exposure ratio and the signal-to-noise ratio corresponding to each candidate exposure ratio to obtain the target exposure ratio.

[0089] For example, see Figure 7 , Figure 7 FIG. 1 is a schematic diagram of signal-to-noise ratios corresponding to various exposure ratios according to an exemplary embodiment of the present application. Figure 7As shown, the signal-to-noise ratios corresponding to various exposure ratios are calculated in the current scene, and the exposure ratio corresponding to the maximum signal-to-noise ratio is selected as 10, and 10 is used as the target exposure ratio.

[0090] It should be noted that Figure 7 This is only an illustrative explanation. In other scenes, the numerical variation curve of the signal-to-noise ratio corresponding to each exposure ratio may be expressed in other forms, and the exposure ratio corresponding to the maximum signal-to-noise ratio may also be other values, such as the maximum exposure ratio selected to be 4, 6, etc.

[0091] Next, some embodiments of exposure adjustment are described.

[0092] In some implementations, the target exposure ratio is directly used for subsequent image acquisition.

[0093] In some embodiments, an exposure ratio value is selected from the middle of a numerical range from a current exposure ratio to a target exposure ratio to obtain a gradient adjustment value; subsequent image acquisition is performed based on the gradient adjustment value, and a new target exposure ratio corresponding to an imaging picture acquired by the gradient adjustment value acquisition is calculated; subsequent image acquisition is performed based on the new target exposure ratio corresponding to the gradient adjustment value, until the calculated new target exposure ratio is equal to the gradient adjustment value used for image acquisition, thereby completing the exposure adjustment.

[0094] For example, if the current exposure ratio is 4, the currently calculated target exposure ratio is 10, and an exposure ratio value is selected from the middle of the numerical range from the current exposure ratio to the target exposure ratio, and the gradient adjustment values ​​obtained include 6, 8, and 10, then the subsequent image acquisition is first performed with the gradient adjustment value 6, and the above steps are repeated based on the imaging picture acquired with the gradient adjustment value 6 to calculate the new target exposure ratio.

[0095] Repeat the above steps until the calculated new target exposure ratio is equal to the gradient adjustment value used for image acquisition, and the exposure adjustment is completed. For example, when a gradient adjustment value of 10 is used for subsequent image acquisition, the above steps are repeated to calculate the new target exposure ratio of 10 based on the image acquired with the gradient adjustment value of 10, and the optimal exposure ratio is obtained, and the exposure adjustment is completed; or, when a gradient adjustment value of 8 is used for subsequent image acquisition, the above steps are repeated to calculate the new target exposure ratio of 8 based on the image acquired with the gradient adjustment value of 8, and the optimal exposure ratio is obtained, and the exposure adjustment is completed.

[0096] By calculating the target exposure ratio while adjusting, a more accurate optimal exposure ratio can be obtained.

[0097] In some embodiments, there are multiple gradient adjustment values. If the new target exposure ratios calculated by accumulating a preset number of gradient adjustment values ​​are the same, the new target exposure ratios corresponding to the preset number of gradient adjustment values ​​are used as the final adjustment value; and the final adjustment value is used for subsequent image acquisition.

[0098] For example, if the new target exposure ratios calculated by three consecutive gradient adjustment values ​​are all 10, then the final adjustment value of the exposure ratio is directly determined to be 10, and the final adjustment value 10 is used for subsequent image acquisition to speed up the exposure adjustment speed.

[0099] Optionally, after adjusting to the final adjustment value, subsequent image acquisition is performed again according to the final adjustment value, and the above steps are repeated to calculate a new target exposure ratio based on the imaging picture acquired with the final adjustment value, and it is verified again whether the new target exposure ratio is equal to the final adjustment value. If so, it indicates that the final adjustment value is the optimal exposure ratio and the exposure adjustment is completed. Otherwise, the above steps are repeated.

[0100] The exposure adjustment method provided by the present application obtains the current imaging picture by performing image acquisition using the current exposure ratio, wherein the current imaging picture is synthesized by a long-frame image and a short-frame image, and the current exposure ratio refers to the ratio between the maximum brightness of the long-frame image and the maximum brightness of the short-frame image; calculates the signal-to-noise ratio corresponding to the current imaging picture to obtain the signal-to-noise ratio corresponding to the current exposure ratio; and obtains a plurality of exposure ratios close to the current exposure ratio value to obtain a plurality of candidate exposure ratios, predicts the signal-to-noise ratio of the current imaging picture at each candidate exposure ratio, and obtains the signal-to-noise ratio corresponding to each candidate exposure ratio; selects the exposure ratio corresponding to the maximum signal-to-noise ratio from the signal-to-noise ratio corresponding to the current exposure ratio and the signal-to-noise ratio corresponding to each candidate exposure ratio to obtain the target exposure ratio; performs subsequent image acquisition based on the target exposure ratio, and selects the final exposure ratio by traversing the signal-to-noise ratios corresponding to various exposure ratios, which is not easy to fall into the local optimal solution, quickly performs exposure adjustment, and improves the signal-to-noise ratio of the adjusted imaging picture.

[0101] Figure 8 FIG. 1 is a block diagram of an exposure adjustment device shown in an exemplary embodiment of the present application. Figure 8 As shown, the exemplary exposure adjustment device 800 includes:

[0102] The current image acquisition module 810 is used to acquire the current image by using the current exposure ratio. The current image is composed of a long-frame image and a short-frame image. The current exposure ratio refers to the ratio between the maximum brightness of the long-frame image and the maximum brightness of the short-frame image.

[0103] The signal-to-noise ratio calculation module 820 is used to calculate the signal-to-noise ratio corresponding to the current imaging picture, and obtain the signal-to-noise ratio corresponding to the current exposure ratio; and obtain multiple exposure ratios close to the current exposure ratio value, obtain multiple candidate exposure ratios, predict the signal-to-noise ratio of the current imaging picture at each candidate exposure ratio, and obtain the signal-to-noise ratio corresponding to each candidate exposure ratio;

[0104] An exposure ratio selection module 830 is used to select an exposure ratio corresponding to a maximum signal-to-noise ratio from the signal-to-noise ratio corresponding to the current exposure ratio and the signal-to-noise ratio corresponding to each candidate exposure ratio, to obtain a target exposure ratio;

[0105] The exposure ratio adjustment module 840 is used to perform subsequent image acquisition based on the target exposure ratio.

[0106] It should be noted that the exposure adjustment device provided in the above embodiment and the exposure adjustment method provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment, and will not be repeated here. In practical applications, the exposure adjustment device provided in the above embodiment can allocate the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0107] See also Fig. 9 , Fig. 9 9 is a schematic diagram of the structure of an embodiment of an electronic device of the present application. The electronic device 900 includes a memory 901 and a processor 902, and the processor 902 is used to execute program instructions stored in the memory 901 to implement the steps in any of the above-mentioned exposure adjustment method embodiments. In a specific implementation scenario, the electronic device 900 may include but is not limited to: a microcomputer, a server, and in addition, the electronic device 900 may also include a mobile device such as a laptop computer and a tablet computer, which is not limited here.

[0108] Specifically, the processor 902 is used to control itself and the memory 901 to implement the steps in any of the above-mentioned exposure adjustment method embodiments. The processor 902 can also be called a central processing unit (CPU). The processor 902 may be an integrated circuit chip with signal processing capabilities. The processor 902 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. In addition, the processor 902 can be implemented by an integrated circuit chip.

[0109] See also Fig.10 , Fig.10 The computer-readable storage medium 1000 stores program instructions 1010 that can be executed by a processor, and the program instructions 1010 are used to implement the steps in any of the above-mentioned exposure adjustment method embodiments.

[0110] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0111] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0112] In the several embodiments provided in the present application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device implementation described above is only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0113] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or part of the contribution to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of each implementation method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program code.

Claims

1. An exposure adjustment method, characterized in that: The method comprises: The current exposure ratio is used to perform image acquisition to obtain a current imaging picture, wherein the current imaging picture is synthesized by a long-frame image and a short-frame image, and the current exposure ratio refers to a ratio between the maximum brightness of the long-frame image and the maximum brightness of the short-frame image; Calculating the signal-to-noise ratio corresponding to the current imaging picture to obtain the signal-to-noise ratio corresponding to the current exposure ratio; and acquiring a plurality of exposure ratios close to the current exposure ratio value to obtain a plurality of candidate exposure ratios, predicting the signal-to-noise ratio of the current imaging picture at each candidate exposure ratio, and respectively obtaining the signal-to-noise ratio corresponding to each candidate exposure ratio; Selecting an exposure ratio corresponding to a maximum signal-to-noise ratio from the signal-to-noise ratio corresponding to the current exposure ratio and the signal-to-noise ratio corresponding to each candidate exposure ratio to obtain a target exposure ratio; Subsequent image acquisition is performed based on the target exposure ratio.

2. The method according to claim 1, characterized in that The method further comprises: Obtaining a brightness signal-to-noise ratio function corresponding to the current exposure ratio and each candidate exposure ratio, wherein the brightness signal-to-noise ratio function is used to describe the signal-to-noise ratio corresponding to different pixel brightnesses; The short frame image or the long frame image corresponding to the current imaging screen is used as a reference image, and a histogram of the reference image is obtained to obtain a reference histogram; wherein the reference histogram is used to describe the number of pixel points corresponding to different pixel brightnesses in the reference image; The calculating the signal-to-noise ratio corresponding to the current imaging picture to obtain the signal-to-noise ratio corresponding to the current exposure ratio includes: Multiplying the brightness signal-to-noise ratio function corresponding to the current exposure ratio and the value corresponding to the brightness of each pixel of the reference histogram, and accumulating the product of the brightness of each pixel to obtain the signal-to-noise ratio corresponding to the current exposure ratio; The predicting the signal-to-noise ratio of the current imaging picture at each candidate exposure ratio, and obtaining the signal-to-noise ratio corresponding to each candidate exposure ratio, includes: The brightness signal-to-noise ratio function corresponding to the candidate exposure ratio is multiplied by the value corresponding to the brightness of each pixel of the reference histogram, and the product of the brightness of each pixel is accumulated to obtain the signal-to-noise ratio corresponding to the candidate exposure ratio.

3. The method according to claim 2, characterized in that The obtaining of the brightness signal-to-noise ratio function corresponding to the current exposure ratio and each candidate exposure ratio includes: Based on the fact that image brightness is proportional to signal-to-noise ratio, a brightness signal-to-noise ratio function of a long frame image and a brightness signal-to-noise ratio function of a short frame image corresponding to each exposure ratio are constructed to obtain a long frame signal-to-noise ratio function and a short frame signal-to-noise ratio function corresponding to each exposure ratio; wherein the product of the slope of the long frame signal-to-noise ratio function corresponding to any exposure ratio and the slope of the short frame signal-to-noise ratio function is equal to the any exposure ratio; For the long frame signal-to-noise ratio function and the short frame signal-to-noise ratio function corresponding to each exposure ratio, the image brightness range is divided into a long frame range, a fusion range and a short frame range in descending order, and the long frame signal-to-noise ratio function and the short frame signal-to-noise ratio function in the fusion range are fused to obtain a fusion function corresponding to the fusion range; The long frame signal-to-noise ratio function within the long frame range, the fusion function within the fusion range, and the short frame signal-to-noise ratio function within the short frame range are sequentially spliced ​​to obtain the brightness signal-to-noise ratio functions corresponding to the respective exposure ratios.

4. The method according to claim 3, characterized in that: The image brightness range is divided into a long frame range, a fusion range and a short frame range from small to large, including: The preset brightness value range in the image brightness range corresponding to the long frame image is used as the fusion range, the brightness range corresponding to the minimum brightness value less than the preset brightness value range is used as the long frame range, and the brightness range corresponding to the maximum brightness value greater than the preset brightness value range is used as the short frame range.

5. The method according to claim 2, characterized in that: The method further comprises: In response to a change in the exposure time of a reference image corresponding to any candidate exposure ratio relative to the exposure time of the reference image corresponding to the current exposure ratio, calculating a ratio between the exposure time of the reference image corresponding to the candidate exposure ratio and the exposure time of the reference image corresponding to the current exposure ratio to obtain a signal-to-noise ratio exposure time coefficient; The step of multiplying the brightness signal-to-noise ratio function corresponding to the candidate exposure ratio by the value corresponding to the brightness of each pixel of the reference histogram, and accumulating the product of the brightness of each pixel to obtain the signal-to-noise ratio corresponding to the candidate exposure ratio includes: Multiplying the brightness signal-to-noise ratio function corresponding to the candidate exposure ratio and the value corresponding to the brightness of each pixel of the reference histogram, and accumulating the product of the brightness of each pixel to obtain an initial sum; The initial sum and the signal-to-noise ratio exposure time coefficient are multiplied to obtain the signal-to-noise ratio corresponding to the candidate exposure ratio.

6. The method according to claim 1, characterized in that The subsequent image acquisition based on the target exposure ratio includes: Selecting an exposure ratio value from a numerical range between the current exposure ratio and the target exposure ratio to obtain a gradient adjustment value; Performing subsequent image acquisition based on the gradient adjustment value, and calculating a new target exposure ratio corresponding to the imaging picture acquired by the gradient adjustment value acquisition; Subsequent image acquisition is performed based on the new target exposure ratio corresponding to the gradient adjustment value, until the calculated new target exposure ratio is equal to the gradient adjustment value used for image acquisition, thereby completing the exposure adjustment.

7. The method according to claim 6, characterized in that The number of the gradient adjustment values ​​is multiple, and the method further includes: If there are a preset number of gradient adjustment values ​​accumulated and the new target exposure ratios calculated are the same, the new target exposure ratios corresponding to the preset number of gradient adjustment values ​​are used as the final adjustment value; The final adjustment value is used for subsequent image acquisition.

8. The method according to claim 1, characterized in that The subsequent image acquisition based on the target exposure ratio includes: The target exposure ratio is directly used for subsequent image acquisition.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the processor is configured to execute program instructions stored in the memory to implement the steps in the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and the program instructions can be executed by a processor to implement the steps in the method according to any one of claims 1 to 8.

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