150 results about "Color enhancement" patented technology

The invention relates to a color enhancement method and device, equipment and a storage medium, and the method comprises the steps: responding to an original RGB value of a to-be-displayed image of a screen, and detecting the illuminance of the current environment of the screen; a first three-dimensional color lookup table is obtained in response to the situation that the illuminance meets a preset condition, and the first three-dimensional color lookup table is constructed based on an original color gamut of the screen; and performing gamut mapping on the original RGB value based on the first three-dimensional color lookup table to obtain a target RGB value of the to-be-displayed image, the target RGB value being used for the screen to display the to-be-displayed image. According to the invention, the display color of the screen can be enhanced by using a larger color gamut when the illuminance meets the preset condition, the color display precision of the to-be-displayed image can be maintained, the image blurring is avoided, and the user experience can be improved.

The invention belongs to the field of computer vision, and particularly relates to a cloud cover density calculation method combining image features and superpixel partition merging and a corresponding device thereof. The method comprises the following steps: 1, preprocessing an original image P0 containing the sky and the cloud layer to obtain a gray level image H1, an HSV image P1, a Lab image P2, a color enhancement image E1 and a gray level enhancement image E2 which correspond to different color spaces; 2, P0 is divided into a plurality of superpixel areas, and background elimination is carried out by combining characteristic information of H1, P1, P2, E1 and E2; gradually combining the superpixel regions belonging to the cloud layer or the sky; and 3, extracting values of an S channel and a Lab color space three channel of the superpixel region, generating a four-dimensional feature vector, and inputting the four-dimensional feature vector into a K-means algorithm for clustering. And 4, calculating the cloud cover density according to the clustering result of the K-means algorithm. The problem that the sky and cloud layer image segmentation task is difficult to process by the existing traditional image segmentation technology is solved.

The invention provides a weak light environment image contrast improvement method for a robot welding path, and relates to the field of image processing, and the method comprises the steps: firstly improving an original compression mode based on a brightness single factor through a contrast perception compression factor; secondly, a low-light image enhancement color space based on a polar coordinate structure is introduced, and a contrast perception compression factor is combined to construct a low-light image enhancement color space with a space decoupling capability; a welding path structure response diagram is constructed by fusing the direction consistency entropy and the direction responsivity, the sensitivity to the structural characteristics with directivity and coherence such as the welding path is enhanced, and the accuracy and robustness of structure extraction are improved; and finally, through combination of the robot welding path image after color enhancement and the welding path structure response diagram, background noise interference is effectively inhibited while welding edge details are highlighted, accurate enhancement of the welding path image is realized, the welding edge details are enhanced, and the definition and the structure continuity of the image are improved.

Embodiments of the present application provide a color evaluation-based image color enhancement method and apparatus, and a device. The method comprises: acquiring an input first image, performing image segmentation processing on the first image, to obtain multiple segmentation regions, and performing color space conversion processing on the first image, to obtain a second image of a preset color space mode; based on a component value of a color space corresponding to a segmentation region in the second image, calculating a regional color evaluation value of each segmentation region; by means of a configured intensity prediction module, determining a color adjustment coefficient corresponding to the regional color evaluation value; based on the color adjustment coefficient, performing color enhancement on the first image. The image color enhancement method can adapt to complex and changeable live broadcast scenarios, the complexity of color enhancement processing is low, and the color enhancement effect is improved.

The invention provides a self-adaptive real-time image quality enhancement method and system, and the system comprises an image data conversion module which is used for converting YUV format data of a video stream into an RGB format through OpenGL, and further converting the RGB format into an HSL color space; the adaptive brightness enhancement module dynamically adjusts an enhancement coefficient according to the brightness component of the current frame; the multi-frame joint skin color detection module is used for determining a skin color area based on the chromaticity and saturation threshold range of the HSL color space; the local self-adaptive color enhancement module is used for inputting image generation dynamic parameters and performing color enhancement; the noise analysis and noise reduction module is used for performing soft threshold processing on the high-frequency signal; the texture adaptive detail enhancement module is used for adjusting sharpening strength based on texture complexity; and the GPU acceleration processing framework is used for rendering and outputting the enhanced RGB image in real time. Through a series of technical innovations, the image quality of video playing and the user experience are remarkably improved in multiple aspects, and the method has important practical application value.

The invention relates to a real-time image quality optimization method and system for intelligent display equipment, and the method comprises the following steps: carrying out the real-time sensing of the preset environment illumination intensity and watching distance of the display equipment, and obtaining a scene parameter data stream; performing dynamic brightness mapping on a target display image in the display device based on the scene parameter data stream to obtain a brightness compensation curve; performing pixel-level modulation on the target display image based on the brightness compensation curve to obtain image enhancement data; performing color space conversion on the image enhancement data to obtain a color enhancement matrix; gray scale voltage modulation is carried out on the display panel based on the color enhancement matrix, an optimized display image is obtained, and the technical problem that overexposure or color distortion of an image is prone to occurring in a strong light or weak light environment due to the fact that a traditional image enhancement technology is difficult to adapt to a complex and changeable viewing environment is solved.

The invention provides an auxiliary correction method and system for color vision disorder, and the method comprises the following steps: S1, carrying out the color and texture decoupling of an input image through a perception consistent state space module, and extracting a global color distribution feature and a perception invariant texture feature; generating a personalized enhanced image in combination with dynamic color offset adjustment and CVD perception quality evaluation; s2, constructing a dynamic heterogeneous marshalling architecture, dynamically combining a high-precision model and a lightweight module, and realizing a self-adaptive network architecture with multi-level complexity and performance balance through frequency domain sensing decoupling, heterogeneous multi-dimensional static kernel and implicit neural frequency domain acceleration technologies; and S3, acquiring image data in real time by adopting an embedded hardware platform, optimizing a processing flow through a dynamic complexity threshold constraint model, outputting an enhanced image in combination with AR / VR equipment and a standardized interface, and guiding continuous optimization of a personalized color enhancement algorithm through effect analysis evaluation feedback.

The embodiment of the invention provides an ore type identification method and system based on a deep learning model, and the method comprises the steps: collecting a plurality of ore images, unifying the sizes of the collected images, constructing an ore image data set, and dividing the ore image data set into a training set and a test set; preprocessing the ore image, including cutting, overturning, color enhancement and standardization processing of the image to obtain an input tensor; a channel attention module is embedded by taking DenseNet161 as a backbone, high-dimensional spatial features are reserved, and a main classifier and an auxiliary classifier are arranged at the tail end in parallel to construct an ore recognition model; based on the training set, label smoothing, mixing precision training and an AdamW optimizer are adopted to carry out end-to-end training on the ore recognition model, and the model is verified through the test set; and inputting a to-be-identified ore image into the trained model, outputting a category probability by the main classifier, and taking the category corresponding to the maximum value as an identification result.

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 discloses a video color enhancement method and device based on deep learning guidance multi-lookup table fusion, equipment and a storage medium, and relates to the technical field of video enhancement, and the video color enhancement method based on deep learning guidance multi-lookup table fusion comprises the steps: obtaining a plurality of target three-dimensional lookup tables through an image classification network; obtaining a pixel adaptive weight through a sensing network; obtaining an initial pixel value of original video data, and determining a plurality of undetermined pixel values corresponding to the initial pixel value according to the plurality of target three-dimensional lookup tables; and processing the plurality of undetermined pixel values according to the pixel adaptive weights to obtain a target pixel value. According to the invention, the color hopping phenomenon between video frames after color enhancement can be reduced.

The invention provides a color enhancement method based on an image brightness gain, and the method comprises the steps: S1, inputting a YUV color image, and obtaining an original brightness image y; s2, brightness enhancement: enhancing the brightness image y by adopting histogram equalization to obtain an enhanced image Y; s3, calculating a weighting curve, and calculating a Gauss weighting curve according to a preset parameter delta; s4, gain calculation: calculating a brightness gain according to the change of each pixel position before and after brightness enhancement; s5, color enhancement: carrying out color enhancement on the input u and v data images to obtain enhanced chrominance component images U and V; s5.1, the UV component deviates in the negative direction, the input u data and v data are deviated, the deviated data u'and v 'are obtained, and the formulas are u' = u '-128 and v' = v-128; s5.2, UV component enhancement is carried out, u and v data images are enhanced by using the brightness gain yGain2, and the formula is u '= u' * yGain2 and v '= v' * yGain2; and S5.3, the UV component is shifted in the positive direction, the enhanced color component is subjected to data shifting again, final U and V numerical values are obtained, and the formulas are U = u '+ 128 and V = v' + 128.

The invention discloses a low-light image enhancement method based on multi-branch feature fusion. The method comprises the steps of obtaining low-light and normal-light images, and dividing training and test sets; processing the normal light image to obtain a single-channel color-enhanced, brightness-enhanced and texture-enhanced component graph; obtaining a multi-branch feature fusion low-light image enhancement neural network model; and inputting a to-be-measured low-light image into the model to enhance the backbone network, and outputting a restored image under a normal illumination condition. According to the invention, a color dynamic calibration module is provided, and a plug-and-play color correction normal form is provided for the model; an external source wound modulation module is provided, and key information can be focused while interference information such as noise is restrained. A brand new shared convolution is provided, so that the model can adaptively adjust the characteristic response intensity of each channel. An exogenous creative shared residual block structure is constructed, so that the enhanced backbone network can utilize the internal feature abstraction ability and the external auxiliary information guide advantage at the same time.

In implementations of techniques and systems for color enhancing digital images to match templates, a processing device implements a color effects module to receive a digital image to be inserted into a template. The color effects module identifies one or more dominant colors of the digital image and the template. The color effects module determines a harmonic match between the image dominant colors and the template dominant colors. The processing device then outputs a modified digital image to which the image dominant color of the harmonic match is applied as a color effect.

The invention relates to the field of optical imaging and computer vision, discloses a large-view-field binocular image combination dynamic parameter adjusting system, and aims to solve the problems that a traditional head-up display is limited in view field and monochromatic display and information response are lagged. The system comprises a left and right eye image acquisition and preprocessing unit, a target positioning and trajectory prediction unit, a binocular parallax compensation and registration unit, a dynamic fusion display control unit, a color enhancement and information superposition unit, an eye movement tracking feedback unit and a parameter adaptive adjustment unit. Through binocular wide-angle acquisition, three-dimensional target prediction, sub-pixel-level image registration, panoramic stitching fusion, semantic color coding and dynamic parameter adjustment of eye movement driving, 120-degree * 60-degree distortionless panoramic display, multi-source information intelligent layered superposition and visual focus area adaptive enhancement are realized. According to the method, the air combat situation sensing efficiency and the weapon response speed are improved, and the method has high real-time performance (delay of lt, 80ms), strong environmental adaptability and engineering integration.

The present invention provides a physical whiteboard perspective method and a virtual whiteboard generation method. By using the Hoffman line detection method and counting the number of overlapping lines, the judgment dimension of the whiteboard-related lines is increased, the accuracy of line extraction is improved, and then the accuracy of virtual whiteboard acquisition is improved, and the position of the whiteboard is better identified. On the basis of generating a high-precision virtual whiteboard, the purity of the whiteboard color is improved by color enhancement processing, and the virtual whiteboard corresponding to each frame of the physical whiteboard image is processed according to a preset algorithm to obtain a background image, a motion image and a color difference image, and then a foreground mask is obtained. The foreground mask of the current frame, the color enhancement picture of the current frame and the full perspective picture of the previous frame are used to achieve a smooth perspective effect of the character. The present invention not only reduces the computational complexity of the virtual whiteboard generation algorithm and the whiteboard perspective algorithm, but also improves the accuracy of detection and perspective, so that users can get a better experience.

The invention relates to the technical field of unmanned aerial vehicle image processing, and discloses an unmanned aerial vehicle image noise reduction and color enhancement optimization method and system. The method comprises the following steps: firstly, acquiring an original image sequence acquired in the flight process of the unmanned aerial vehicle, and identifying corresponding environment illumination intensity change data and motion fuzzy feature distribution; time dimension features of the original image sequence are divided based on environment illumination intensity change data, and a noise feature distribution map is constructed in combination with motion blur feature distribution; and finally, calling an equipment imaging parameter set of the original image sequence, analyzing a corresponding imaging mode constraint condition, and formulating a multi-stage noise reduction strategy framework for the original image sequence according to the constraint condition and the noise feature distribution map. The method realizes effective noise reduction and color enhancement of the unmanned aerial vehicle image by comprehensively considering the environment illumination change, the motion blur feature and the equipment imaging parameter, improves the image quality, and is suitable for the field of unmanned aerial vehicle image processing.

Described is an ophthalmic device that contains visible light filters. The ophthalmic device is a free radical reaction product of a reactive mixture comprising: one or more monomers suitable for making the ophthalmic device; a first visible light filtering compound, the first visible light filtering compound having a visible light absorption maximum between 430 and 480 nm and a full width half maximum (FWHM) at the visible light absorption maximum of at least 35 nm and up to 150 nanometers, wherein the compound is photostable, and wherein the compound has a molar extinction coefficient of at least 7740 L·mol−1·cm−1; and a second visible light filtering compound. The devices may provide one or more benefits to wearers, including enhanced macular pigment optical density (MPOD), as well as improved color perception and color enhancement.

The invention discloses an underwater building concrete apparent defect detection method and system based on artificial intelligence. The method comprises the following steps: firstly, carrying out aerial and underwater calibration on preset color and black-and-white cameras; a first image and a second image which are shot by the color camera after calibration and are provided with and not provided with laser stripes and a third image shot by the black-and-white camera are obtained; and matching the first image and the third image based on the laser stripes, constructing a point cloud image, and obtaining a target point cloud image by combining color enhancement of the second image. And finally, identifying the defect of the image by using an apparent defect detection model to obtain a defect detection result, thereby realizing efficient and accurate underwater building concrete apparent defect detection.

Described is an ophthalmic device that contains visible light filters. The invention provides an ophthalmic device that is a free radical reaction product of a reactive mixture comprising: one or more monomers suitable for making the ophthalmic device; a first visible light filtering compound, the first visible light filtering compound having a visible light absorption maximum between 430 and 480 nm and a full width half maximum at the visible light absorption maximum of at least 35 nm and up to 150 nanometers, wherein the first visible light filtering compound is photostable, and wherein the first visible light filtering compound has a molar extinction coefficient of at least 7740 L·mol−1·cm−1; and a second visible light filtering compound having a visible light absorption maximum between 480 and 530 nm and a full width half maximum of at least 50 nanometers and up to 150 nanometers. The reactive monomer mixture may also contain a third visible light filtering compound. The devices may provide one or more vision benefits, including enhanced macular pigment optical density as well as improved color perception and color enhancement and diminished visual artifacts.

The application discloses a backdoor attack method based on color frequency injection and adaptive local enhancement, and relates to the technical field of machine learning and artificial intelligence security. The method comprises the following steps: introducing low-frequency color offset and weak high-frequency signal into an image in a CIELAB color space to perform global color-frequency injection; using a pre-trained proxy model to locate a high-sensitive perception domain of the model through mixed evaluation of gradients and class activation maps, and generating a binary mask; in an HSV color space, respectively applying nonlinear stretching factors to saturation and brightness of the sensitive domain based on the mask to perform adaptive local enhancement; using Gaussian smoothing, adaptive noise and histogram matching to eliminate edges and statistical abnormalities caused by local enhancement, completing compensation color enhancement to generate a poisoned image; and modeling a trigger core parameter as a constrained optimization problem, and using a particle swarm optimization algorithm to jointly dynamically update the trigger core parameter to obtain an optimal strategy. The application anchors the trigger feature depth in the core semantic area of the model and lurks in the normal data manifold, guarantees a high attack success rate, realizes extreme visual and feature concealment, and has strong anti-defense robustness.

This invention relates to the field of image processing technology, and more particularly to an image processing method, apparatus, computing device, and computer-readable storage medium. The method includes: processing a first image using a convolutional neural network to obtain a global semantic information map of the first image; based on the global semantic information map, enlarging the grayscale image of the first image to obtain a first reconstructed image; based on the global semantic information map, performing color enhancement processing on the chroma image of the first image to obtain a second reconstructed image; and fusing the first reconstructed image and the second reconstructed image to obtain a second image. This allows the second image to be reconstructed synchronously with the first image in terms of super-resolution and color enhancement, resulting in a higher degree of consistency between the first and second reconstructed images, thus improving the quality of the second image. Simultaneously, reusing global semantic information also saves computational resources.

This invention belongs to the field of image processing technology, specifically relating to a method for color enhancement of medical images. The method includes: (1) acquiring an image; (2) additive decomposition of the image; (3) stretching and merging the channels of the brightness and detail layers; (4) performing mask calculations on the merged image; (5) color enhancement of the image; (6) fusing the color-enhanced image with the original image; and (7) color-coding the fused image to obtain the final enhanced image. The color enhancement effect of the image after image fusion processing is relatively smooth, the visual effect of the processed image is prominent, and the color accuracy of the image is also good.

The invention relates to an LCD (liquid crystal display) color enhancement method and related equipment, and the method comprises the following steps: collecting original color data of an LCD screen, and carrying out pixel-level division on the original color data to obtain a plurality of pixel color subsets; performing RGB color gamut mapping conversion on the pixel color subset to obtain a color gamut mapping subset, and performing color distribution statistics based on the color gamut mapping subset to obtain a color distribution histogram; when the proportional unbalance degree of the RGB three-channel components in the color distribution histogram exceeds a standard deviation range, performing color gain calculation on the original color data based on the color distribution histogram to obtain a color gain matrix; and color output adjustment is performed on a backlight unit of the LCD screen based on the color gain matrix to obtain an enhanced LCD display color, and the technical problem that local over-enhancement or color distortion is easily caused due to lack of dynamic perception and response to pixel-level color distribution characteristics of actual display content of the screen in a traditional color enhancement method is solved.

The invention discloses a traditional Chinese medicine face image color quantitative characterization method fusing color space and texture features, and relates to the technical field of image processing and medical image analysis. The method comprises the following steps: performing illumination normalization and noise suppression processing on a facial image of a patient to obtain an enhanced and denoised image, and constructing a structure response graph and a color fusion feature tensor; introducing a structure-guided color enhancement mechanism, and obtaining a structure-enhanced multi-channel color tensor based on the structure response diagram and the color fusion feature tensor; constructing a yellow saliency guide map and a multi-scale texture response tensor, and generating a joint feature map through a color-texture joint decoupling mechanism under saliency weighting; normalizing the combined feature map, and extracting texture features and colors of the normalized combined feature map; and combining the color and texture features into a joint quantitative feature vector, and quantitatively representing the color of the traditional Chinese medicine facial image. The problems that color distinguishing is unstable and color and texture decoupling is difficult under complex illumination are solved.

The present application relates to a semi-supervised remote sensing image change detection method based on pseudo-bi-temporal generation technology. The method comprises: processing a small amount of single-phase remote sensing image data and building extraction labels thereof by pseudo-bi-temporal data generation technology, generating a large amount of pseudo-bi-temporal remote sensing images and pseudo change detection labels to provide label data support for the training of the change detection network; using a semi-supervised method, increasing or decreasing the color difference between the features of the bi-temporal remote sensing image pair by color enhancement in the strong enhancement process, deforming the ground objects by shape enhancement in the strong enhancement process, forcing the change detection network model to make consistent change detection results for the unlabeled remote sensing images before and after the strong enhancement, effectively utilizing a small amount of labeled single-phase remote sensing image pairs and a large number of unlabeled remote sensing image pairs to improve the change detection accuracy of remote sensing images, overcoming the problem of insufficient labeled data, and releasing the potential of remote sensing big data.

The invention provides a color enhancement method and device, equipment and a storage medium, and relates to the technical field of image processing. The method comprises the following steps: acquiring a color index value corresponding to each pixel point in each dimension in an image to be processed; based on the color index value of each pixel point in each dimension, traversing a first preset color lookup table LUT associated with the dimension to obtain a target type of a memory color corresponding to the pixel point; traversing a second preset LUT according to the target type of the memory color to which each pixel point belongs and the corresponding color index value, so as to obtain a color adjustment amount corresponding to the pixel point; and based on the color adjustment amount corresponding to each pixel point, performing color adjustment on each pixel point to obtain a color-enhanced target image. Therefore, when the to-be-processed image is subjected to color enhancement, the target image can better meet the expectation of a user for the color through memory color enhancement, and the user experience is improved.

The invention relates to a video processing method and device, computer equipment and a readable storage medium. The method comprises the following steps: acquiring continuous original image data frames in a low-illumination environment, and acquiring original noise distribution of the original image data frames; performing noise reduction processing on the original noise distribution to determine noise reduction features in the original image data frame; performing color enhancement processing on the original image data frame to determine a color enhancement feature in the original image data frame; carrying out adaptive feature fusion on the noise reduction features and the color enhancement features to obtain a fusion feature map; performing time domain alignment on adjacent frames before and after the target frame to the target frame to obtain an alignment feature map; and performing pixel enhancement processing on each frame of image in the alignment feature map to obtain a pixel enhancement frame corresponding to each frame of image so as to determine a target video. Therefore, the brightness of the low-illumination video is improved, the noise is remarkably suppressed, the color authenticity is enhanced, the time sequence stability is kept, and the quality and the real-time performance of the video are improved.

This invention discloses a robust detection method for AI-generated videos based on gradient-guided contrastive learning, comprising the steps of: constructing and training a spatial feature extractor; using the trained spatial feature extractor to train a temporal feature extractor and a classification head; inputting the video to be detected into the trained spatial feature extractor, temporal feature extractor, and classification head to obtain the probability that the video to be detected is an AI-generated video; and obtaining the detection result according to a set detection threshold. The beneficial effects of this invention are that by employing a gradient-guided curriculum learning strategy to construct color-enhanced video frame samples, and by using a supervised contrastive learning method to train the spatial feature extractor and the frame sequence samples to train the temporal feature extractor, the robustness of the AI-generated video detection method to color-related post-processing can be improved; and by using a curriculum learning strategy to dynamically adjust the intensity of color enhancement during training, higher training stability and robustness are achieved.

The invention discloses an image color enhancement method and device, equipment and a storage medium, and relates to the technical field of computer vision, and the image color enhancement method comprises the steps: receiving a to-be-enhanced image, and extracting the image features of the to-be-enhanced image; performing fine processing on a preset three-dimensional lookup table feature through the image feature to obtain a content perception toning feature; performing color perception enhancement processing on the image features through the preset three-dimensional lookup table features to obtain color perception semantic features; performing fine-grained enhancement processing on the content perception toning features to obtain target perception features; and performing color enhancement on the to-be-enhanced image according to the target perception feature and the color perception semantic feature to obtain a target image. According to the invention, refined color adjustment of image content perception can be realized, and the visual quality of image color enhancement is improved.

The present application relates to an image processing method, device, electronic device, and computer-readable storage medium. The image processing method includes: obtaining a user's enhancement preference information; obtaining an image to be processed, and dividing the image to be processed into multiple areas to be enhanced according to the hue distribution of the image to be processed; and performing corresponding color enhancement processing on each of the areas to be enhanced according to the enhancement preference information to adjust the color attributes of the image to be processed. By dividing the image to be processed into multiple areas to be enhanced based on the hue distribution, corresponding color enhancement processing can be performed for different hues, thereby effectively avoiding the problem of color distortion in the process of color enhancement. Moreover, combined with the obtained user enhancement preference information, a personalized and accurate color enhancement effect can be provided based on the user's preferences, thereby realizing an image processing method with a high degree of personalization and high color reproduction.