311 results about "Tone mapping" patented technology

The invention discloses a high-precision image processing method and system based on illumination adaptive compensation, and relates to the technical field of computer vision and image processing, and the method comprises the steps: inputting an original image, and dividing the image into a high-frequency edge layer, an intermediate-frequency texture layer and a low-frequency illumination layer through a multi-scale residual network; acquiring illumination intensity, color temperature and scene categories in real time by using an ambient light sensor and a scene semantic segmentation model, and generating dynamic compensation parameters; carrying out dynamic range expansion on a low-frequency illumination layer based on a physical illumination model, and adjusting the weight of highlight suppression and dark area enhancement through a self-adaptive S-shaped exposure curve; a double-branch generative adversarial network is adopted, noise suppression and super-resolution reconstruction are carried out on the high-frequency layer, and texture detail enhancement is carried out on the intermediate-frequency layer; aligning the data of the depth camera and the infrared sensor with the visible light image through a cross-modal fusion module; and performing tone mapping on the fused image based on human visual characteristics, and outputting an enhanced image with a high dynamic range and reserved details.

A display device including: a display; and at least one processor connected to the display and configured to control the display device, where the at least one processor is configured to: identify an input image as a plurality of areas, identify a type of each of the plurality of areas by identifying at least one of the plurality of areas as a focus area and by identifying other areas of the plurality of areas as a background area, obtain a global tone mapping curve (TMC) for the input image based on a pixel level reduction amount corresponding to a target power consumption reduction amount, allocate a power consumption reduction amount to each of the plurality of areas based on the type of each of the plurality of areas, obtain a local tone mapping curve for each of the plurality of areas.

The invention provides a pixel dynamic calibration method and device for a display, equipment and a storage medium. The method comprises the following steps: acquiring display parameters and a calibration image of the display; performing visual semantic analysis on the calibration image to generate a partition guidance mask, and marking a plurality of areas with different visual fidelity in the image by the partition guidance mask; generating a group of parallel region tone mapping curves for the plurality of regions based on the display parameters and the partition guide mask; performing synchronous processing on a corresponding region of the calibration image by using the region tone mapping curve to generate a partition mapping result, and fusing the partition mapping result into a correction image; generating a strategy execution record based on the partition guidance mask and the regional tone mapping curve; binding the strategy execution record with the content characteristics of the calibration image, and storing as a scene calibration strategy file; and when a new image frame is processed, calling the matched scene calibration strategy file for processing.

The invention relates to the technical field of image shooting and processing, and discloses an anti-overexposure image processing method and system under strong light based on a high dynamic range, and the method comprises the steps: collecting a plurality of frames with different exposures for the same scene through an extremely weak light full-color night vision device, carrying out the multi-frame fusion, and generating a high dynamic range image; the method comprises the following steps: mapping an image with a high dynamic range to display equipment with a low dynamic range, and readjusting the brightness and contrast of the image to realize tone mapping; different areas in the image are independently processed, an overexposure area is identified, the brightness of the overexposure area is independently reduced, and the original brightness and contrast of other areas are kept; histogram equalization parameters are dynamically adjusted according to the local brightness distribution of the image, the image contrast and details are improved, and image color correction is achieved. The problem of overexposure of the ultra-weak light full-color night vision device in a strong light environment is solved through a high dynamic range algorithm, and the image quality of the night vision device under different illumination conditions is remarkably improved.

The invention relates to a high dynamic range image tone mapping method and device based on an attention mechanism. The method comprises the following steps: S1, determining a first brightness channel; the first brightness channel is a brightness channel of the high dynamic range image. And S2, adaptively adjusting the global brightness of the first brightness channel based on a channel attention mechanism to obtain a first brightness feature map subjected to global brightness mapping. And S3, enhancing local details of the first brightness feature map based on a self-attention mechanism to obtain a second brightness feature map. And S4, obtaining a second brightness channel according to the first brightness channel and the second brightness feature map. The second brightness channel is a brightness channel of the low dynamic range image. According to the method, adaptive brightness modification is realized based on channel attention, the flexibility of brightness mapping can be improved, detail retention in tone mapping is enhanced based on a self-attention mechanism, global brightness mapping and local detail enhancement can be effectively integrated, and a more excellent tone mapping result is obtained.

To employ low resolution, noisy tone mapping operations for high resolution images, at least one raw image frame is converted to a first image at a higher resolution and a second image at a lower resolution. Tone mapping is applied to the second image to derive a third image at the lower resolution. Histogram matching and regularization are performed to determine a lookup table approximating histogram matching of the second image to the third image. A global gain map is derived based on luma for the first image and the lookup table. A local gain map is derived by up-sampling and denoising residual differences between the third image and the lookup table applied to the second image. Based on the global gain map and the local gain map, a total gain map is determined for tone mapping the first image to produce a fourth image at the first resolution.

A method includes receiving a quantized video having a first maximum luminance level or brightness associated with a mastering display with which the quantized video was mastered. The method also includes de-quantizing the quantized video in order to generate a de-quantized video. The method further includes applying a scene-adaptive tone mapping function to the de-quantized video in order to generate a tone mapped video. In addition, the method includes displaying the tone mapped video on a target display. The tone mapped video has a second maximum luminance level or brightness associated with the target display, and the second maximum luminance level or brightness is less than the first maximum luminance level or brightness.

A method includes obtaining a high dynamic range (HDR) image and generating low dynamic range (LDR) images based on the HDR image, where at least some of the LDR images are associated with different exposure levels. The method also includes generating tone-type weight maps based on the LDR images, where at least one of the LDR images is associated with two or more of the tone-type weight maps. The method further includes generating blending weights for the LDR images based on the tone-type weight maps, where the blending weights for at least one of the LDR images are based on at least two tone-type weight maps associated with at least two of the LDR images.

A non-transitory computer-readable storage medium stores executable instructions that, when executed by a processor, cause performance of operations comprising operations to access an image captured by an image sensor, obtain a transfer function for mapping pixel values, determine a faces indication that reflects a proportion of a scene depicted in the image that includes one or more human faces, and modify the transfer function based on the faces indication. Modifying the transfer function based on the faces indication comprises adjusting a gain of the transfer function to move the gain closer to unity. The operations include to apply the transfer function to pixel values of the image to produce a tone mapped image and output the tone mapped image.

The invention provides a semantic segmentation method based on an HDR dynamic neural radiation field, and relates to the technical field of image segmentation. According to the semantic segmentation method based on the HDR dynamic neural radiation field, the segmentation capability of a two-dimensional visual large model on pictures and videos is utilized, the segmentation capability of the two-dimensional model is expanded to four dimensions, a hyper-parameter controlled tone mapping module is constructed, mapping from an HDR view to LDR is controlled, and the segmentation capability in an HDR scene is greatly improved; a mask deformation field network and a dynamic mask inverse rendering method are provided, expensive labeling does not need to be performed on a four-dimensional scene, and any object in the four-dimensional dynamic scene can be segmented by using a two-dimensional picture mask.

The invention discloses an HDR dynamic tone mapping method and system. The HDR dynamic tone mapping method comprises the following steps: S1, acquiring an ambient light parameter, a user interaction parameter and a display equipment state parameter which are acquired in real time; s2, performing dynamic parameter fusion on the ambient light parameters, the user interaction parameters, the display equipment state parameters and video content metadata to generate dynamic metadata; s3, generating a self-adaptive tone mapping curve based on the dynamic metadata, wherein the self-adaptive tone mapping curve adjusts a brightness mapping relation through an equipment aging compensation parameter; and S4, measuring the error between the actual display image quality and the theoretical value, and correcting the compensation parameter in the dynamic parameter fusion according to the error value. According to the method, various parameters are collected in real time, the adaptive tone mapping curve is generated through dynamic fusion, and errors are corrected in combination with closed-loop feedback, so that the problem of image quality degradation caused by environment change, equipment aging and user interaction difference in the traditional HDR technology is solved, and high-precision and adaptive dynamic display optimization is realized.

The invention discloses an image coding method, an image decoding method and related devices, and belongs to the field of image processing. The method comprises the following steps: acquiring a first image, wherein the first image is an image needing tone mapping; based on the first image, determining position coordinates of a plurality of sampling points of a tone mapping curve corresponding to the first image through an optimization equation or a deep learning network model; and compiling the first image and the position coordinates of the plurality of sampling points into a code stream. According to the method, the optimization equation or the deep learning model is established based on at least one of the image contrast, the image brightness and the contrast human eye perception threshold, and the image needing tone mapping is coded through the optimization equation or the deep learning model. Therefore, the decoding end establishes the tone mapping curve based on the code stream and realizes tone mapping of the image, so that the display effect of the image is obviously improved.

The invention relates to the technical field of image processing, in particular to an image data desensitization method, device and equipment, and the method comprises the steps: extracting a to-be-processed image or a RAW domain image of a to-be-processed video stream; sequentially performing wide dynamic range processing, first Bayer noise processing and tone mapping processing on the RAW domain image to obtain a first processed image; obtaining a dynamic ROI detection model and a privacy grading model; based on the first processing image and the dynamic ROI detection model, a second processing image is output, and the second processing image is that a privacy area is marked on the first processing image; based on the second processing image and a privacy grading model, obtaining a privacy level comprehensive score of each privacy area; and determining a corresponding desensitization means based on the privacy level comprehensive score of each privacy area so as to carry out pixel level privacy processing on the second processing image, and realizing end-to-end security privacy protection by processing the RAW domain image of the video or the image.

This disclosure provides a tone mapping method and apparatus. In the method, after global tone mapping is performed on a to-be-displayed image, local mapping information is adjusted to obtain the adjusted local mapping information; and based on the adjusted local mapping information, local tone mapping is performed on at least some pixels in the to-be-displayed image on which global tone mapping is performed. In embodiments of this disclosure, a global and local joint tone mapping solution is used, and local mapping is performed based on the adjusted local mapping information, to balance an uncoordinated contrast between different regions in local tone mapping, and effectively improve tone mapping effect and display effect of an image.

The invention provides an image processing method, and the method comprises the steps: carrying out the conversion processing of a to-be-processed YUV image in a first color gamut range, obtaining an RGB image in a second color gamut range, generating a target mapping function, and enabling the target mapping function to be a function obtained through the fusion processing of a first mapping function and a second mapping function, the first mapping function is a function obtained by performing histogram statistics according to a Y channel component of the to-be-processed YUV image, and the second mapping function is a preset function, and performing mapping processing on an image in the to-be-played video based on the target mapping function to obtain a target RGB image. According to the scheme, the RGB pixels are combined with the brightness, the brightness of the image after tone mapping is increased, and the image quality of the image is improved.

Disclosed is a method for enabling exposure-guided three-dimensional (3D) reconstruction, including (i) receiving Low Dynamic Range (LDR) input image captured by camera; (ii) rendering High Dynamic Range (HDR) image corresponding to input image, using HDR 3D reconstruction model pre-trained for HDR image reconstruction of 3D environment; (iii) applying HDR to LDR tone-mapping operator on HDR image, for producing tone-mapped LDR image; (iv) determining first loss function (FLF) between input image and tone-mapped LDR image, FLF comprising weighted pixel value differences between pixels of input image and corresponding pixels of tone-mapped LDR image; (v) determining whether input image has saturated pixel(s), saturated pixel(s) comprises: highlight saturated pixel, shadow saturated pixel; (vi) de-weighting pixel value difference corresponding to saturated pixel in FLF; (vii) back-propagating gradient of FLF with respect to model parameters, through differentiable render function of HDR 3D reconstruction model, for adjusting model parameters in way that reduces FLF.

The invention discloses an equipment calibration method and device, computer equipment and a computer readable storage medium, and the method comprises the steps: calibrating a gamma curve of target display equipment when an HDR function module is in a closed state; after the gamma curve calibration is completed, the HDR function module is controlled to be started; and when the HDR function module is in the on state, calibrating the tone mapping curve of the target display equipment. By adopting the method, the color accuracy effect of the image output by the target display equipment can be improved.

The invention discloses a night enhanced imaging and dynamic denoising method for an automobile data recorder, relates to the technical field of vehicle-mounted image processing and computational photography, and is used for solving the problems of insufficient brightness and too strong noise of night videos of the automobile data recorder under a low-illumination condition. Estimating the zero-mean Gaussian noise intensity of a read link and the Poisson noise intensity increasing along with the brightness in the same frame, converting the zero-mean Gaussian noise intensity and the Poisson noise intensity into brightness equivalent parameters, and introducing a unified control function G as the scale reference of all enhancement and noise reduction links; defining a sharing priori H and carrying out unified constraint in dense optical flow, depth time sequence denoising and local tone mapping; and in combination with the motion mask and the background model, performing differential space-time denoising and proportional write-back, and in cooperation with motion blur detection, spatial variation motion kernel and constrained deconvolution and multi-frame detail recharge, obtaining target brightness and outputting an RGB frame after proportional write-back.

The invention discloses a self-adaptive high-dynamic-range image generation method in a chemical monitoring scene, and belongs to the technical field of high-dynamic imaging and computational imaging. Performing registration preprocessing on the acquired reference image based on a brightness consistency criterion; constructing a high dynamic range image generation network model, inputting the reference image into the model, performing feature extraction and fusion through a multi-scale network, and generating a high dynamic range image based on the fused features; performing tone mapping on the generated high dynamic range image based on a brightness consistency criterion; and comparing the generated high dynamic range image with a reference image, calculating a pixel-level loss function and multi-dimensional perception loss, and optimizing an image generation process through a back propagation training network model. According to the method, imaging details of different scales are captured through a multi-scale feature processing method, so that the generated image is better in brightness range and details, and the expressive force of the image under a complex illumination condition is ensured.

Systems and methods for providing a temporal noise reducer (TNR) architecture that improves TNR performance by adjusting for lighting differences between image frames. Temporal noise reduction is a core feature of a video processing pipeline, where TNR can be used to decrease noise in video streams. Some TNRs compensate for the motion of objects between frames, but lighting changes can also lead to additional noise. As an object's position moves from frame to frame, the lighting of the object may change. A lightweight tone-mapping and color-correction operator is added to the TNR feedback loop, matching the radiometric properties of a TNR reference frame to the radiometric properties of the current frame to compensate for radiometric differences, thereby increasing the effectiveness of TNR in handling incremental lighting changes and speeding up its response to abrupt lighting changes.

A computing system for generating a gain map-based enhanced High Dynamic Resolution (HDR) image receives an HDR image comprising a plurality of pixels, each pixel having one or a plurality of brightness values for each of a plurality of color components, applies at least a first tone mapping algorithm to each pixel in the HDR image, generates a gain map using the HDR image and the SDR image, generates a gain map-based enhanced HDR image based on the SDR image and the gain map, and generates an output based on the gain map-based enhanced HDR image.

The invention relates to the technical field of augmented reality, in particular to a 24-solar-term astronomical space-time presentation method and system based on mixed reality. The method comprises the following steps: firstly, reading augmented reality scene initialization data, configuring virtual scene parameters, and executing three-dimensional scene registration to generate a registration matrix; executing visual tracking by collecting an image stream, and generating a viewpoint transformation matrix; the system performs real-time sky semantic segmentation on an image stream to generate a binarized sky mask, and constructs an augmented reality rendering pipeline: synthesizing registration and viewpoint matrix calculation rendering operators, using the sky mask as an Alpha mask to perform pixel-level fusion of a virtual starry sky and a real sky, and combining sparse point clouds to perform depth occlusion synthesis, so as to obtain an augmented reality rendering pipeline. An atmospheric optical coloring model and a dynamic volume particle system are loaded for environmental atmosphere enhancement; and optical consistency fusion of a virtual image and a real environment is further realized through HDR luminosity alignment and tone mapping, and finally high-fidelity augmented reality superposition is realized.

The invention provides an image processing method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: acquiring a source domain image, and performing tone mapping on the source domain image to obtain a target domain image; performing brightness enhancement on the source domain image to obtain a brightness enhanced image; identifying a target object in the source domain image, and determining a first weight of the target object in the source domain image; determining a second weight of the target object in the brightness enhancement image and a third weight of the target object in the target domain image based on the first weight; and based on the second weight and the third weight, fusing the brightness enhanced image and the target domain image to obtain a fused image. According to the invention, the accuracy of image processing can be improved.

The embodiment of the invention provides an image processing method, electronic equipment, a storage medium and a program product. The image processing method comprises the following steps: determining the original pixel brightness of a current image according to image data to be processed; dividing the original pixel brightness into a plurality of data branches; obtaining a histogram corresponding to the original pixel brightness of each data branch; determining a tone mapping curve of each data branch based on a tone mapping model and the histogram; fusing the tone mapping curve of each data branch to obtain a fusion curve; obtaining target pixel brightness corresponding to the original pixel brightness based on the fusion curve; and determining target image data corresponding to the to-be-processed image data based on the original pixel brightness and the target pixel brightness. According to the scheme of the embodiment, automatic tone mapping is realized, and the flexibility and the adaptability of tone mapping are improved.

The invention belongs to the technical field of image processing, and particularly relates to a wide dynamic image fusion method and device for a fast moving scene. A single-lens double-light-path light splitting method is adopted, optical systems with the same type of sensors, the same exposure duration and different aperture coefficients are adopted for double light paths, field angles are matched, scenes are the same, and the error is smaller than 0.5 pixel; the method comprises the following steps: performing video image acquisition, performing wavelet transform on acquired two paths of image data, decomposing two paths of image matrixes to obtain a high-frequency detail image and a low-frequency-width dynamic image, and performing multi-depth-of-field fusion on the high-frequency detail image; performing multi-exposure fusion on the low-frequency wide dynamic image to generate HDR wide dynamic data, and performing tone mapping to form an LDR low-frequency image; and combining the high-frequency fusion detail image and the LDR low-frequency image. According to the method, the image without overexposure, with large depth of field and multiple details can be obtained, and the effects of video processing tasks such as target searching and tracking are effectively improved.

A method for processing an image captured by an image sensor. The method comprises receiving first pre-tonemapped intensity values of a first image captured by the image sensor. The method comprises applying a first tonemapping function having a first tonemapping strength to the first pre-tonemapped intensity values to generate first tonemapped intensity values. The method comprises receiving second pre-tonemapped intensity values of a second image captured by the image sensor, the second image captured subsequent to the first image. The method comprises, based on a difference between the first tonemapped intensity values and a target for the first tonemapped intensity values, determining a second tonemapping function having a second tonemapping strength. The method comprises applying the second tonemapping function to the second pre-tonemapped intensity values to generate second tonemapped intensity values.

The invention discloses a low-illumination image enhancement method based on semantic perception and differentiable tone mapping, and belongs to the field of computer vision and image processing. According to the method, semantic information of each region in an image is extracted through a semantic segmentation network, different importance weights are distributed for different semantic regions, and then a differentiable tone mapping curve generation module is driven to dynamically adjust brightness and contrast enhancement parameters of each region. According to the system, an end-to-end trainable architecture is adopted, semantic perception loss, physical consistency loss and image quality loss are jointly optimized, and finally content self-adaptive enhancement of low-illumination images is achieved. Compared with the prior art, the problems of semantic information loss, overexposure or insufficient enhancement and the like in a traditional method are effectively solved. By introducing semantic perception driven by deep learning and differentiable tone mapping, the detail and color consistency of a semantic region can be more accurately kept in the image enhancement process. In the aspect of performance evaluation, the peak signal-to-noise ratio (PSNR) of the method is improved by more than 3.2 dB, the average intersection-to-union ratio (mIOU) of semantic segmentation is improved by more than 8.5%, and the method is remarkably superior to an existing mainstream image enhancement method.

The embodiment of the invention provides an image processing method and related equipment. The method comprises the following steps: performing tone mapping on an SDR image to obtain an HDR image; acquiring a pixel value of each pixel point in the HDR image; pixel points in the HDR image are grouped according to a preset pixel point grouping strategy, and brightness information of each group of pixel points is acquired; obtaining depth information of each pixel point in the HDR image; dividing the HDR image into a plurality of image areas, wherein the minimum image area is a pixel point; determining target display screen brightness based on the current display screen brightness and the maximum display screen brightness; determining brightness adjustment coefficients and depth adjustment coefficients of pixel values of pixel points in the plurality of image areas, and adjusting the pixel values of the pixel points in the plurality of image areas to obtain a target HDR image; and adjusting the display screen brightness to the target display screen brightness, and displaying the target HDR image. According to the embodiment of the invention, the image can be subjected to high-dynamic range expansion according to the depth information, and the image main body is effectively highlighted.

The embodiment of the invention relates to the technical field of terminals, in particular to a method for generating an HDR image and electronic equipment, and can improve the display effect of the HDR image. The method comprises the following steps: acquiring a first HDR image, and performing tone mapping on the first HDR image to obtain a second HDR image; the first HDR image comprises a first highlight area, and the first highlight area comprises a highlight object; the second HDR image comprises a second highlight area, and the second highlight area corresponds to the first highlight area; correcting the gradient direction of the first pixel in the second highlight area into the gradient direction of the second pixel in the first highlight area to obtain a third HDR image; the second pixel corresponds to the first pixel; wherein the gradient direction of one pixel is positive and negative of the difference value between the brightness value of the next pixel of the pixel on a first direction axis and the brightness value of the pixel, and the first direction axis comprises an x axis and a y axis; wherein the dynamic imaging range of the third HDR image is smaller than that of the first HDR image, and the third HDR image is used for displaying.