128 results about "Local color" patented technology

The invention relates to an underwater low-illumination image enhancement method based on local color correction and a multi-scale variation decomposition model, and belongs to the technical field of image processing. The method comprises the following steps: firstly, performing color correction on an input underwater low-illumination image; next, the corrected image is converted from an RGB color space to an HSV color space, a multi-scale variational decomposition model is applied to a V channel in the image, and a base layer, two detail layers and an atomization layer are generated; then, the atomization layer is separated, self-adaptive brightness equalization is carried out on the base layer, an enhanced base layer is obtained, self-adaptive detail sharpening is carried out on the detail layer, and an enhanced detail layer is obtained; and finally, linearly fusing the enhanced base layer and detail layer to obtain an optimized V channel, converting the image back to an RGB color space, and outputting an enhanced underwater image. According to the method, the overall enhancement of the image is realized, the contrast, the brightness and the detail expression are remarkably optimized, and the visual effect of the underwater image is remarkably improved.

The invention relates to the technical field of three-dimensional scanning, and discloses a binocular three-dimensional scanning system and method with a texture mapping function. The system comprises a depth data acquisition module, a depth data correction module, a point cloud topology construction module, a texture mapping engine module and a mapping optimization module. The depth data acquisition module synchronously acquires a left view and a right view of a target object through a binocular camera array, and generates an original depth image data set; the depth data correction module performs abnormal value detection on the original depth data, extracts discrete distribution through aggregation boundary analysis and replaces abnormal values; the point cloud topology construction module generates a three-dimensional point cloud spatial topology structure, and associates point cloud coordinates with left view pixel positions; the texture mapping engine module projects left view RGB data to the point cloud to generate an initial texture map; and the mapping optimization module verifies the local color consistency through the generative adversarial network, and outputs an optimized three-dimensional texture model. The system improves the texture precision and authenticity of the three-dimensional model.

The invention provides a multi-agent collaborative visual completion method and system based on semantic communication, and the method comprises the steps: a first agent collects an occluded overall original image through a visual sensor, generates an occluded region mask through a lightweight semantic segmentation network, extracts mask semantic information, and transmits the mask semantic information to a second agent; the second intelligent agent matches the global semantic feature of the second intelligent agent with the received mask semantic information, locates a sheltered area, then cuts the sheltered area to obtain a semantic feature vector of the area, and returns the semantic feature vector to the first intelligent agent; and after the first intelligent agent receives the image, performing high-fidelity reconstruction on the occlusion area by using a conditional diffusion model to obtain a complemented image with consistent semantics, and performing weighted fusion and local color matching and splicing on the complemented image and the original image to obtain a global perception result. According to the scheme, perceptual information sharing and occlusion area reconstruction with low bit rate, high robustness and high real-time performance can be realized under limited communication resources.

The invention provides a mobile phone screen color gamut control method and system, and the method comprises the steps: collecting the spectral characteristics of ambient light, and constructing a spectrum matrix; constructing a dynamic reference color model based on the spectrum matrix to generate a global color gamut adjustment matrix, and dynamically adjusting the screen color gamut according to the global color gamut adjustment matrix; collecting eye movement data of a user in real time, constructing a fixation area, and performing area color gamut optimization analysis on the fixation area in combination with the dynamic reference color model; according to an optimization analysis result, local compensation is carried out in combination with the global color gamut, and a final local color gamut matrix is generated; the energy consumption of the non-gazing area is reduced, and unnecessary color processing is reduced. The invention provides an intelligent and dynamic color gamut self-adaptive adjusting system by combining environment spectrum detection and eye movement tracking technologies, overcomes the defects of the prior art in the aspects of environment light adaptability, color accuracy and energy efficiency, and has remarkable innovativeness and practical value.

The invention relates to the technical field of data analysis and evaluation, in particular to a color fastness evaluation method and system, and the method comprises the following steps: setting test conditions to simulate external factors encountered in the actual use or processing process of textiles; under the simulated test condition, treating the textile sample for a preset time; acquiring a multispectral image of the textile by adopting a multispectral imaging technology, and capturing color change of the textile; developing a self-adaptive image enhancement algorithm, and performing preprocessing; comparing spectral information of the multi-spectral image of the textile before and after processing based on chromatic aberration evaluation analysis so as to quantify the color fastness; analyzing the multispectral image after image enhancement by using a deep learning technology, extracting color attenuation features, and comprehensively evaluating a color fastness score; according to the color fastness evaluation method, basic color difference information is considered, advanced features such as textures, edges and local color modes of the textile are further included, and the accuracy and reliability of color fastness evaluation are remarkably improved.

The invention relates to the technical field of defect detection, in particular to a chip defect detection method and system based on a Gaussian attention mechanism, and the method comprises the steps: collecting a chip image, carrying out the preprocessing of the collected chip image, marking a defect region in the chip image, and generating a binary mask image reflecting the defect distribution; based on the binary mask image, combining distance transformation and local color change analysis to generate a Gaussian attention channel; connecting the Gaussian attention channel with the chip image in the channel dimension to generate an enhanced image; and executing a defect detection algorithm based on the enhanced image, and identifying a defect region in the chip image. According to the method, the sensitivity of the detection system to tiny and low-contrast defects can be improved, so that the false alarm rate is reduced, and meanwhile, the defect detection precision is effectively improved on the premise that the calculation complexity is not greatly increased.

The invention provides a local color difference self-adaptive image demosaicing interpolation method, a system-level chip and a computer storage medium. The method comprises the following steps: acquiring an original RAW image; taking a current target pixel in the RAW image as a reference, and extracting a cache window; in the cache window, gradient values of the current target pixel in the horizontal direction, the vertical direction, the diagonal direction and the reverse diagonal direction are calculated; determining an interpolation direction according to the gradient value; and along the interpolation direction, carrying out RGB full-channel interpolation calculation. In the interpolation process, according to the difference between the center point and the two ends of the main direction, the weights of the pixel points participating in interpolation are obtained in a self-adaptive mode, the interpolation precision can be guaranteed, meanwhile, the size of a calculation window is reduced, the resolution of the image can be effectively improved in the interpolation calculation process, the quality of the demosaiced image is improved, and the image quality is improved. The consumption of hardware resources can be saved, and the problem that the image quality is poor due to the fact that an interpolation method adopted in an existing demosaicing process is poor in precision and high in hardware consumption is solved.

The invention belongs to the technical field of printing, and particularly relates to a printing ink color real-time calibration method and system based on dynamic color gamut mapping, high-frequency online monitoring is achieved by constructing a local color gamut contour and arranging a multispectral sensing array, and the change trend can be captured at the initial stage of color shift; a stable color block recognition and local weighted mapping updating mechanism is combined, an updated lookup table is dynamically generated, and correction is prevented from being triggered by dependence on significant offset; further, according to the response relationship between the color offset direction and the printing parameters, the adjustment of the ink key opening and the printing pressure is implemented in stages, the offset source is inhibited from the physical level, the mapping adjustment is started when the color difference does not exceed the limit, the equipment parameter fine adjustment is synchronously driven, the response time is remarkably shortened, the generation of out-of-tolerance products is reduced, and the product quality is improved. And the real-time performance and the stability of color control are improved.

The invention discloses a chromaticity automatic gain control method and device for a composite video signal, and belongs to the technical field of signal processing, and the method comprises the steps: obtaining a chromaticity signal adjusted by a historical chromaticity gain parameter, and demodulating the chromaticity signal from the chromaticity signal to obtain a chromaticity component reference value of a color synchronization signal; calculating an actual color synchronization level and a phase deviation between an input color subcarrier and a local color subcarrier according to the color difference component reference value; in response to the chroma phase deviation meeting a preset chroma phase locking condition, generating a chroma phase locking identifier, and calculating a chroma gain error according to the actual color synchronization level; and determining a chrominance gain control stable identifier according to the chrominance gain error, and determining an output chrominance gain parameter based on the chrominance gain control stable identifier, the chrominance phase locking identifier and the chrominance gain error so as to dynamically adjust the next line of gain through closed-loop control. According to the invention, the influence of long-distance cable transmission on the composite video chrominance signal can be reduced, and the chrominance difference of different transmission distances is reduced.

The invention discloses an intelligent dot matrix electronic display screen control system, relates to the technical field of electronic display screens, and solves the technical problems that color adjustment and brightness adjustment are inaccurate and are difficult to adapt to different scene requirements. Duty ratio adjustment for limiting a brightness change threshold value is adopted for a low-gray-scale area, the jump problem caused by traditional linear distribution is avoided, dark part details are displayed more smoothly, different reasons such as overall color cast, local color cast and dynamic content color cast are distinguished through a color adjustment control unit, a differential adjustment strategy is adopted, the color rendition degree is improved, and the color rendition effect is improved. According to the method, pixel-level partition detection is adopted, abnormal pixels are accurately screened, a compensation coefficient is calculated, the problem of local brightness deviation is solved, a thermal attenuation coefficient is introduced in combination with the relation between temperature and brightness, the compensation coefficient is dynamically corrected, and the brightness uniformity error in a high-temperature environment is reduced.

The invention discloses an underwater image enhancement method and device based on color perception fusion attention and background CD-ROM separation contrast learning. The method comprises the following steps: extracting color information in an underwater original image based on a designed color extraction module; inputting the color information into a local color recovery branch to obtain reasonably distributed color weight information; performing triple attention branching on the underwater original image to recover details and fuzzy information in the image, and obtaining an underwater enhanced image with rich detail texture information; and inputting the reasonably distributed underwater enhanced image with rich color weight information and detail texture information into a global light recovery branch, adjusting the global light characteristics of the underwater image by adopting a contrast learning strategy, and finally obtaining a recovered underwater image enhancement result. According to the method, the enhancement performance in the underwater scene can be effectively improved, the degradation influence of the underwater complex environment on the image can be eliminated, and the color and texture details of the image can be truly restored.

The invention relates to the field of image processing, and provides an image processing method and electronic equipment, the method is applied to the electronic equipment, a light source of a shooting environment where the electronic equipment is located is a stroboscopic light source, and the method comprises the following steps: running a camera application program of the electronic equipment; a first image is displayed, the first exposure time is shorter than the first duration, and the first image comprises stripes and / or color cast image areas; detecting a first operation; in response to the first operation, obtaining a second image, the second exposure time being an integral multiple of the first duration; obtaining a color conversion matrix and / or a brightness parameter based on the first image and the second image; performing first image processing on the first image based on the color conversion matrix and / or the brightness parameter to obtain a third image, the third image being an image in which stripes and / or color cast image areas are removed; and displaying or storing the third image. Based on the scheme of the invention, stripes and / or local color cast in the image can be removed when the moving object is shot, and the image quality is improved.

The invention provides a tomato leaf health status assessment system and method, and relates to the technical field of tomato leaf health assessment, and the method specifically comprises the steps: firstly, carrying out the preprocessing of a tomato leaf image, converting the image into a Lab color space, and carrying out the segmentation and extraction of a leaf region based on a chromaticity threshold value; and then leaf area chromaticity channel statistical magnitude is counted, pixel-level anomaly detection is carried out through an adaptive threshold value, and an abnormal area mask and a normal area mask are generated. Performing connected domain analysis on the abnormal region, extracting local color and texture features, and extracting corresponding features of the normal region as a reference; the health deviation degree is obtained by calculating the feature distance between each abnormal region and the normal region, and the comprehensive health assessment index is generated by combining the area proportion of the abnormal regions after normalization. And finally, judging a health state according to a preset threshold rule and outputting an evaluation result. According to the invention, a comprehensive and accurate decision basis is provided for agricultural producers, and the automation level and evaluation accuracy of tomato disease monitoring are improved.

The embodiment of the invention discloses a video processing method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of video processing, and the method comprises the steps: judging whether a to-be-played video meets a preset image quality enhancement condition or not at a decoding stage of the to-be-played video; if the image quality enhancement condition is satisfied, carrying out resolution enhancement on the to-be-played video; performing local color monitoring on the enhanced to-be-played video to obtain a monitoring result; and performing local color enhancement or dynamic frame insertion processing on the video to be played according to the monitoring result. Therefore, the display effect of the low-resolution video can be remarkably improved in the decoding stage, improvement is carried out in the aspects of resolution, color, frame rate and the like, parameters such as resolution, color, frame rate and the like are dynamically adjusted, and the display effect of the low-resolution video is improved.

The invention provides a point cloud registration method and system based on color enhanced geometric information, relates to the technical field of computer vision processing, and solves the problem that an existing point cloud registration mode is poor in registration effect and easy to lose efficacy in a weak feature scene. The method comprises the following steps: acquiring key points of a source point cloud and a target point cloud, using an average color value as a color information reference, and acquiring a local color geometric feature CGD of each key point; the global distance distribution feature similarity between corresponding key points in the source point cloud and the target point cloud is preliminarily determined, then correct corresponding matching point pairs are obtained, and a transfer quantity matrix equation is constructed; and obtaining a transfer quantity after solving, constructing a transfer matrix based on the transfer quantity, updating the target point cloud, and completing registration of the source point cloud and the target point cloud after multiple iterations until the transfer quantity converges. According to the method, colors, local geometric information and global distribution features are combined, and a good registration result can be obtained even under the condition that the geometric features are weak.

The invention discloses an image processing method, a color picking device and a storage medium, and relates to the technical field of image processing, and the disclosed image processing method comprises the steps: obtaining a to-be-processed image, and dividing the to-be-processed image into a plurality of sub-processed images; determining a white balance parameter corresponding to each sub-processed image; according to the brightness of each sub-processing image, carrying out differential processing on the white balance parameter corresponding to the corresponding sub-processing image; and carrying out white balance adjustment on colors corresponding to the sub-processed images sent to the display equipment based on the white balance parameters after differential processing. The technical problem of low image processing quality caused by the phenomena of local color cast and detail missing of the image when a global white balance parameter is adopted for adjustment of the existing image is solved, local color cast of the whole image can be eliminated through the technical scheme, meanwhile, image details are reserved, and the image processing quality is improved.

The invention discloses an abnormal state identification method, system and device based on local color area mutation, and belongs to the technical field of safety monitoring. The method comprises the following steps: extracting a detection frame from a video stream according to a preset unequally spaced frame sequence; positioning local key areas (such as shoulders) of the body of the target constructor in the detection frame; in the region, extracting a maximum aggregation connected domain of target safe color pixels through color analysis, and calculating the area of the maximum aggregation connected domain; and comparing the ratio of the area to the total pixel area of the detection frame with a fixed preset threshold value, and if the ratio exceeds the threshold value, judging that an abnormal state exists and triggering an alarm. According to the method, the local color area mutation is taken as an agent index, a fixed parameter strategy is combined, the problems of large calculation amount, easy environmental interference and complex deployment in the prior art are solved, and the method has the advantages of low calculation cost, strong robustness, fast identification response and easy deployment.

The invention relates to the technical field of display screen color correction, and provides a film and television shooting LED display screen color correction method and system, and the method comprises the steps: obtaining a real-time video stream from a camera; generating color priority region identification information based on the real-time video stream; setting a corresponding target reference color for the identified color priority area; acquiring pixel data of the color priority area from the real-time video stream according to the color priority area identification information; comparing the pixel data of the color priority area with a target reference color to obtain a color deviation; generating a local color compensation scheme according to the color deviation; performing color transition processing on the non-priority area by using a local color compensation scheme to obtain an input signal sent to the LED display screen; and applying the local color compensation scheme to an input signal in real time so as to realize color correction of the LED display screen. The method has the advantages that the picture reality sense and the manufacturing efficiency are improved.

The present application provides an image processing method, device and electronic device. In the technical solution of the present application, an image to be processed is obtained; the image to be processed is processed by the first branch of a pre-trained neural network model to obtain a first type of parameter, and the first type of parameter is used to perform global color processing on the image; the image to be processed is processed by the second branch of the neural network model to obtain a second type of parameter, and the second type of parameter is used to perform local color processing on the image; the image to be processed is color processed according to the first type of parameter and the second type of parameter to obtain a color-processed image. In the above technical solution, the input of the neural network model is the image to be processed, and different branches output different types of parameters, which can avoid the error accumulation problem in the parameter calculation process and obtain more accurate parameters. Furthermore, the image to be processed is processed according to the obtained parameters, which can improve the image color correction effect.

The embodiment of the invention discloses an image detection method, an image detection device and a storage medium, and is applied to the technical field of image detection. The method comprises the following steps: acquiring a first grayscale image and a second grayscale image of any two to-be-detected areas on the surface of a preset object; obtaining a gray scale difference image between the first gray scale image and the second gray scale image; performing median filtering on the gray difference image to obtain a corresponding chromatic aberration distribution diagram; and setting an image defect threshold value corresponding to the chromatic aberration based on the chromatic aberration distribution diagram, and performing defect detection on the surface of a preset object by using the image defect threshold value. The gray difference value is used as the color difference between the gray images of the to-be-detected area, the corresponding image defect threshold value is set based on the color difference, and when the image defect threshold value is used for defect detection, the influence of local color difference on the surface of an object can be reduced, and the defect detection accuracy is improved.

1. The name of the design product: local color chip combination on color chip disc. 2. The use of the design product: color chip combination is pasted on color chip disc, used for changing the color of light passing through it; color chip disc can be rotated in the interior of lamp, used for changing the color of light on the main light path of lamp. 3. The design points of the design product: in shape. 4. The picture or photo that can best show the design points: perspective view 1. 5. The design product is thin, omitting the left view; the design product is thin, omitting the right view; the design product is thin, omitting the top view; the design product is thin, omitting the bottom view. 6. Other circumstances that need to be explained: the gray part in the view is not the part to be protected, and the colored part (not including color) is the part to be protected.

This invention discloses an underwater image enhancement method and apparatus based on color perception fusion attention and background light removal contrast learning. The method includes: extracting color information from the original underwater image using a designed color extraction module; inputting the color information into a local color recovery branch to obtain reasonably distributed color weight information; passing the original underwater image through a triple attention branch to restore details and blur information in the image, obtaining an underwater enhanced image rich in detail and texture information; inputting the reasonably distributed color weight information and the underwater enhanced image rich in detail and texture information into a global light recovery branch, and using a contrast learning strategy to adjust the global light characteristics of the underwater image, finally obtaining the restored underwater image enhancement result. This invention can effectively improve the enhancement performance in underwater scenes, while eliminating the degradation effect of complex underwater environments on images, and realistically restoring the color and texture details of the image.

The present disclosure provides a method for generating an infrared image. The method comprises the step of processing pixel values ​​provided at the output of an image sensor, the image sensor comprising a two-dimensional pixel array, the two-dimensional pixel array comprising a mixture of IR pixels and color pixels. The processing step comprises: - for each color pixel, determining (S3) a local color correlation ratio, which represents the infrared light receiving portion in the color pixel; and estimating (S4) an infrared portion value from the color pixel value based on the local color correlation ratio; and - generating (S5) an infrared image based on the pixel value from the IR pixel and the estimated infrared portion value from the color pixel.

Embodiments of the present application provide an image processing method and device and electronic equipment, and relate to the technical field of image processing. An image to be processed is obtained. The color cast intensity of the image to be processed is determined according to the distribution of specified pixel values of each pixel point in the image to be processed. If the color cast intensity meets a preset color cast requirement, the color cast type of the image to be processed is determined, and the color cast direction of the image to be processed is determined based on the distribution. The color cast type includes global color cast or local color cast. A color cast correction strategy matched with the color cast type and the color cast direction is used to perform color cast correction on the image to be processed to obtain a target image. Compared with related technologies, the scheme provided in the embodiments of the present application can improve the image processing efficiency.

The invention provides a general plane graph color migration method and system based on a depth diffusion model, and the method comprises the following steps: (1) carrying out the semantic segmentation of a black and white general plane line draft graph, and generating a semantic mask graph; (2) extracting global style features of the color general plane reference image and local color distribution features of different semantic regions; (3) inputting color features of the line draft image, the semantic mask image and the reference image into a depth diffusion model, restraining the structure of the generated image to be consistent with that of the line draft through a ControlNet module, and migrating the color features of the reference image to a corresponding position of the line draft according to a semantic region through a region perception attention mechanism; and (4) in the de-noising process of the diffusion model, combining the style consistency loss function and the regional color histogram matching loss to generate an image after color migration. According to the method, the color of the reference picture is migrated to the black-and-white line draft according to the semantic region, meanwhile, the line draft structure is kept complete, and the requirement for rapid personalized drawing can be met.

The application is suitable for the technical field of image processing, and provides an image local color adjustment method and device, electronic equipment and storage medium, the method comprises the following steps: converting a first to-be-adjusted image obtained from an RGB color space to an HSL color space, adjusting the local color of the first to-be-adjusted image in the HSL color space, obtaining a calibrated HSL lookup table according to a plurality of sets of adjustment values obtained after adjustment, calibrating a preset three-dimensional lookup table based on the calibrated HSL lookup table to obtain a calibrated three-dimensional lookup table, and adjusting the color of a second to-be-adjusted image according to the calibrated three-dimensional lookup table, so that the local color of the image is adjusted, and the quality of the whole image is improved.

The application discloses a kind of based on frequency domain perception and illumination self-adaptive unsupervised low-light image enhancement method, comprising: extracting the basic feature of low-light picture by basic feature encoder;The illumination feature of low-light picture is extracted by illumination feature encoder;By frequency domain feature extraction module, the frequency domain feature that is focused on detail texture is converted to basic feature;By color histogram feature extraction module, the local color feature of low-light picture is obtained;By illumination feature decoder, illumination information distribution is learned;By basic feature decoder, the detail texture information of enhanced picture is recovered.The application makes full use of the spatial basic feature and frequency domain detail feature of low-light image, while using adaptive convolution mechanism to learn the illumination condition of different regions, effectively improve the visual quality of enhanced picture.

This invention discloses a two-stage low-light image enhancement method and apparatus based on the HVI color space. The method includes: converting a low-light RGB image to the HVI color space to obtain a luminance feature map and a color feature map; performing global denoising on the luminance feature map and global denoising and global color correction on the color feature map through feature extraction and bidirectional interaction between features; performing local detail enhancement on the initially enhanced luminance feature map and local color correction on the initially enhanced color feature map through bidirectional interaction between features extraction and local features, obtaining fully enhanced luminance and color feature maps; and reconstructing the fully enhanced luminance and color feature maps into a low-light enhanced RGB image. This method achieves a dynamic balance between noise suppression and detail preservation, enhancing image texture, edges, and other minute structures while accurately repairing local color shifts and color overflow issues.

The invention provides a crystal grain detection method and device, electronic equipment and a storage medium. The crystal grain detection method comprises the following steps: performing down-sampling processing on a target image comprising a to-be-detected crystal grain to obtain a gray level distribution image corresponding to the target image; determining a color difference type corresponding to the target image based on the gray level fluctuation range of the gray level distribution image corresponding to the target image; when the color difference type corresponding to the target image is the overall color difference, adjusting the target image based on the reference image; when the color difference type corresponding to the target image is the local color difference, adjusting the target image based on the gray level distribution image and the reference image; and based on the adjusted target image and the reference image, determining whether the to-be-detected crystal grain has defects or not. According to the scheme, differential processing is carried out on the overall chromatic aberration and the local chromatic aberration, so that the adaptability of the scheme to an actual scene is higher, and the accuracy of defect detection on the to-be-detected crystal grains can be improved.

The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and / or retrieving information associated with biological objects having irregular shapes. In some embodiments, the systems and methods partition an input image into a plurality of sub-regions based on localized colors, textures, and / or intensities in the input image, wherein each sub-region represents biologically meaningful data.