1103 results about "Color correction" patented technology

The invention provides a remote sensing strip mine area detection method based on a double-branch structure and a feature fusion mechanism, and the method comprises the steps: obtaining multi-source high-resolution remote sensing image data of a strip mine mining area, carrying out the preprocessing of atmospheric correction, radiation calibration, color correction, image registration, cutting operation and the like, and obtaining time sequence remote sensing image data; a deep learning algorithm is adopted to construct a strip mine area detection model based on a double-branch structure and a feature fusion mechanism, training is carried out through the time sequence remote sensing image data, a remote sensing image detection model is obtained, the double-branch structure comprises a feature extraction branch and a feature generation branch, and the feature extraction branch comprises a feature extraction branch and a feature fusion branch; the feature fusion mechanism comprises a cross attention fusion module and a feature adaptive fusion module; inputting to-be-detected remote sensing image data into the remote sensing image detection model to obtain a detection result of the strip mine mining area. According to the method, the recognition precision of small target details and mining area boundaries of low-resolution images is improved, and the calculation efficiency and the detection precision are both considered.

The invention provides a deep learning self-calibration system for high-precision color management, and relates to the technical field of color calibration, a system architecture comprises an algorithm layer, a system architecture layer and a data processing and optimizing layer, through fusion of multiple advanced algorithms in the algorithm layer, the precision of color correction is effectively improved, and the accuracy of color correction is improved. The high consistency of colors of printed matters in the whole production process is ensured, the quality stability of printed products is greatly improved, data processing is carried out through all modules of the data processing and optimizing layer, real-time data in the printing production process are efficiently processed, a model is dynamically updated, and the printing production efficiency is improved. The design of the system architecture layer provides a comprehensive adaptation scheme for large-scale production, multiple links and multiple equipment materials in the printing industry, the universality and flexibility of the system are enhanced, and the system is suitable for large-scale production, multiple links and multiple equipment materials in the printing industry. The time and labor cost for readjusting the system due to the change of equipment materials is reduced, and the production strain capacity of enterprises is improved.

The invention relates to the technical field of display screen color adjustment, and discloses a display screen color adjustment method and system based on environment information, and the method comprises the steps: obtaining display content data, environment color data, environment light data and a display screen type of a display screen; calculating color perception data according to the environment color data and the display screen type, and performing data processing on the color perception data to obtain chromaticity feedback data; establishing a relationship between the ambient light data and user preferences through a support vector machine method to obtain user evaluation data; inputting the chromaticity feedback data and the user evaluation data into a color correction model to obtain color adjustment data; classifying the display content data and calculating a type weight index through a squirrel optimization algorithm to obtain a weight index; and generating a color adjustment instruction according to the color adjustment data and the weight index, and performing adaptive adjustment on the display content. The method solves the problems that the color adjustment of the display screen has high requirements on a light source environment and is strong in subjectivity.

The invention relates to an underwater image enhancement method and system. The method comprises the following steps: acquiring a basic underwater image data set and a corresponding reference enhancement image set; forward diffusion and reverse denoising are carried out on the underwater images in the basic underwater image data set and the corresponding reference enhanced image set through a diffusion model; gaussian noise is gradually added to the underwater image in the forward diffusion process, and the degradation process of the underwater image is simulated; stepwise iterative denoising is carried out on the data added with the Gaussian noise in the reverse denoising process; a color correction module is introduced, and color correction and detail enhancement are carried out on the image in the reverse denoising processing process; and constructing a denoising network based on a Transform model, removing noise in the underwater image by combining forward diffusion and reverse denoising processes of the diffusion model, and outputting a high-quality enhanced underwater image.

The invention discloses a panoramic image real-time splicing algorithm and system based on multi-sensor fusion, and particularly relates to the technical field of panoramic image real-time splicing, and the algorithm comprises the following steps: constructing a structured fusion sequence based on multi-source images, postures and position information, optimizing a matching effect through high-density feature extraction and repeated texture recognition, and obtaining a multi-source image fusion sequence; a dynamic foreground and a static background are distinguished by using sparse optical flow so as to improve the visual angle estimation precision, pose fusion optimization is realized in combination with a multi-mode residual error, and the continuity and stability of a spliced image are improved through edge smoothing, brightness tuning and color correction; according to the method, the structured fusion sequence is constructed through multi-source data alignment, so that the data synchronization and splicing stability is improved; identifying repeated regions based on texture direction features, and optimizing feature matching accuracy; and through edge smoothing, brightness harmonizing and color consistency processing, the visual coherence and output quality of the panoramic image are enhanced.

The invention belongs to the field of screen color gamut correction, and discloses a mobile phone screen color gamut correction method and system, and the method comprises the steps: S1, obtaining ambient light data and screen content data; s2, acquiring color correction parameters based on the ambient light data and the screen content data; s3, obtaining personalized correction parameters based on the color correction parameters; s4, obtaining partition correction parameters based on the personalized correction parameters; s5, acquiring an adaptive feedback correction parameter based on the partition correction parameter; and S6, obtaining the display color of the screen based on the adaptive feedback correction parameter. The invention provides a nonlinear color gamut mapping model so as to ensure that parameters after color correction can be accurately mapped to a color gamut range capable of being displayed by a screen. In the mapping process, the nonlinear coefficient of the mapping function needs to be dynamically adjusted according to the actual display characteristics of the screen, and the consistency of color display under different brightness and contrast ratios is ensured.

The invention relates to an edge fusion method and system of an XR immersion type LED display screen. The method comprises the following steps: carrying out feature extraction on image data of an XR immersive LED display screen to obtain an RGB three-channel spatial domain feature matrix and a frequency domain edge feature matrix; performing feature processing to obtain a multi-core context feature map; performing coding processing to obtain a position-brightness comprehensive coding feature matrix; performing multi-scale decomposition to obtain a multi-scale detail feature map; carrying out pixel clustering on the edge region of the XR immersive LED display screen to obtain an edge topological structure diagram and a brightness transition function; and performing dynamic matching on the display frame rate through a PID controller, and performing brightness compensation and color correction on the edge region to obtain edge fusion data. The method has the multi-scale feature extraction capability, the edge fusion problem under different scenes can be adaptively processed, and it is ensured that processing delay meets the real-time display requirement, so that the overall performance of an XR immersive display system is improved.

The invention discloses a tunnel detection method based on a color image, and relates to the technical field of tunnel detection, and the method comprises the following steps: obtaining color image data in a tunnel, and carrying out the illumination condition calibration of the image data, so as to recognize the illumination change caused by natural light, an artificial light source and a light source fault. According to the method, through illumination calibration, adaptive color correction and dynamic contrast enhancement technologies, the problems of image color distortion and detail loss under the complex illumination condition of the tunnel are solved, accurate extraction of color features and clear presentation of details of each region are ensured, and the robustness and precision of defect detection are remarkably improved. In combination with edge detection, color feature extraction, shape analysis and a machine learning algorithm, automatic application of a multi-modal identification technology is realized, misjudgment areas are automatically filtered, a defect detection report with high confidence is generated, and the defect detection report comprises crack length, leakage area and deformation classification. Accurate and comprehensive data support is provided for tunnel safety assessment and maintenance decision.

The invention relates to an underwater image processing technology in the field of underwater autonomous perception, in particular to an underwater low-quality image enhancement method based on a Laplacian pyramid and contrast learning. Comprising the following steps: S1, image input and multi-scale decomposition: decomposing an input image into a low-frequency residual layer and a plurality of high-frequency detail layers through Laplacian pyramid decomposition; s2, inputting the low-frequency residual error layer into a global illumination and color correction sub-module to obtain an enhanced low-frequency residual error layer; s3, inputting the enhanced low-frequency residual error layer and the multi-scale high-frequency detail layer into a frequency domain enhancement feature module to obtain multi-scale enhanced high-frequency detail layer output; s4, performing progressive reconstruction on the enhanced low-frequency layer output and the high-frequency enhancement layer output of each scale according to the inverse process of the Laplacian pyramid; and S5, outputting a result and applying. The method can be used for sharpening optical image data in the operation task of the autonomous underwater vehicle, and the image quality is improved.

The invention relates to an image processing method for an underwater robot heterogeneous imager array, and the method comprises the steps: S1, collecting an original underwater image, and carrying out the brightness adjustment, and obtaining a first image with balanced brightness; s2, analyzing pixel distribution of the first image, and judging and executing de-scattering processing to obtain a second image; s3, performing multi-scale detail enhancement on the second image to obtain a third image with richer details; s4, evaluating color distribution of the third image, and performing color correction to obtain a fourth image; s5, performing adaptive denoising processing on the noise features of the fourth image to obtain a fifth image; s6, according to the contrast characteristic of the fifth image, contrast adjustment is carried out to ensure that a preset threshold value is met, and a sixth image is obtained; s7, edge enhancement processing is carried out on the sixth image, and a seventh image with the clear edge is obtained.By means of illumination compensation, scattering removal and multi-scale detail enhancement, the influence of complex optical conditions of the underwater environment on the image quality is effectively overcome.

The invention relates to the technical field of image processing, in particular to an image acquisition and analysis method and system, and provides the following scheme: obtaining a visible light and near-infrared multispectral image, generating a spectral difference image, and performing weighted fusion to obtain a first image; segmenting a target region based on the fused saliency map, and calculating a pixel reflectance ratio; solving a color mapping matrix according to the reflectance ratio, and carrying out color correction on the target region to obtain a standardized feature image; and extracting characteristic parameters such as spectrums, colors and textures, inputting the characteristic parameters to a multi-branch convolutional neural network, fusing the characteristic parameters through an attention mechanism, and outputting a state classification result and a quantitative index. The cross-spectral imaging difference can be adaptively compensated, and the fusion precision and the analysis stability are improved.

A technique for generating a digital photograph comprises blending two related images, each sampled according to a different illumination environment. The two related images are blended according to a blend surface function that includes a height discontinuity separating two different blend weight regions. Color consistency between the two related images is achieved by spatial color correction prior to blending. The technique enables a digital camera to generate a strobe image having an appearance of consistent color despite discordant strobe and ambient scene illumination.

In some embodiments, a computer-implemented method of accurately reproducing a color of a mixed product is provided. A color management computing system receives one or more inputs indicative of a target color. The color management computing system transmits a command to a product mixing device to dispense color component products to be mixed to create a mixed product of a first color. The color management computing system obtains an image that includes the mixed product. The color management computing system updates a display color correction profile based on a comparison of a captured color of the mixed product in the image to the target color. The color management computing system presents one or more instances on a display device of an adjusted target color indicative of the updated display color correction profile, in response to receiving a subsequent request to change the target color.

The invention provides an image recognition and processing system based on deep learning. The image recognition and processing system comprises an image acquisition module for acquiring original image data; the image preprocessing module performs noise suppression, edge enhancement and color correction on the image; the multi-scale feature extraction module extracts multi-dimensional features through a cascade convolutional layer and a self-attention mechanism; the self-adaptive classification and identification module performs multi-class classification based on a dynamic weight distribution algorithm; the dynamic optimization control module adjusts hyper-parameters in real time through a multi-objective optimization algorithm; the result output module outputs a classification result and a confidence score; and the feedback learning module updates the network weight according to the confidence score. All the modules work cooperatively, and efficient and dynamic image recognition and processing are achieved. According to the method, efficient and accurate image recognition and processing can be realized, and the adaptability, classification accuracy and robustness of the system are improved.

The invention relates to the technical field of optical detection, in particular to an AOI automatic optical detector calibration method and system, and the method comprises the following steps: obtaining a standard color palette RGB reflection reference, collecting a measured value, calculating an offset contrast, extracting image brightness, generating a correction ratio, converting gray level, extracting abrupt change, screening anomalies, and analyzing a brightness trend to generate a finishing record. And formulating a rule matching correction output comparison table. According to the method, the accuracy and reliability of image detection are enhanced through detailed analysis and accurate correction of color offset and brightness difference, the scheme combines real-time light source data and image information, fine changes are effectively identified, the misjudgment rate is reduced, the accuracy of gray and color correction is improved through comprehensive analysis of light intensity offset and regional brightness trend, and the accuracy of image detection is improved. The detection efficiency and quality of a high-density circuit are ensured, the detailed calibration mechanism remarkably reduces detection errors especially in a high-precision environment, and product quality control and the stability of a production process are optimized.

The invention belongs to the technical field of video processing, and particularly relates to a monitoring video enhancement method for a farm, which aims to solve the technical problem of low quality of an enhanced video in the prior art, and comprises the following steps: S1, processing each frame of image in a monitoring video sequence frame by frame; s2, distinguishing a target animal area from a background area, and identifying and generating an artifact mask; s3, aiming at the background area, carrying out key smoothing processing on the artifact position to inhibit the artifact; s4, for the image of the target animal area, performing adaptive nonlinear enhancement on the brightness component, and performing color correction on the chrominance component; s5, performing pixel-level fusion on the enhanced target animal area and the background area; and S6, spreading the information of the previous frame to the current frame by using the forward optical flow field, and carrying out weighted fusion on the information of the previous frame and the current frame. According to the method, the target bred animals, the background areas and the artifacts are accurately distinguished, so that refined and differentiated processing of pictures is realized.

The invention discloses a video image dithering processing method and system based on a rescue scene, and relates to the technical field of video image processing. In order to solve the problems that in the prior art, the jitter compensation effect in a complex dynamic scene is poor, the processing precision cannot meet the requirement of a high-intensity rescue scene, and in a low-light or smog environment, the definition of an image is difficult to effectively improve, so that the visibility of a video picture is insufficient in an extreme rescue scene. Through the steps of video jitter detection, video stream correction, edge processing and video data transmission, the visibility of a video picture in an extreme rescue scene is effectively improved, jitter in video stream data can be accurately identified and corrected, edge distortion generated by jitter compensation is eliminated through edge processing, and the video jitter compensation accuracy is improved. According to the method, the image definition in a low-light environment is remarkably improved through brightness compensation, contrast optimization and color correction processing, the stability and definition of video images in a rescue scene are improved, and more reliable video support is provided for high-intensity rescue actions.

The invention provides a multi-scale polarization data fusion method based on a 4K polarization imaging detector, and relates to the technical field of image processing, and the method comprises the steps: carrying out the adaptive edge detection of four original polarization images, generating a comprehensive edge image, and carrying out the definition scoring; performing color calibration on each original polarization image based on the ambient light intensity and the color temperature, and outputting a polarization image after color calibration and a color deviation score; combining definition scores, color deviation scores and global brightness information entropies of the four polarization images after color calibration to construct a multi-objective optimization function, and solving the multi-objective optimization function in a multi-exposure parameter space to obtain an optimal exposure parameter combination; applying the optimal exposure parameter combination to image capture of the next period, and calculating global quality reward values of four newly collected polarization images; when the global quality reward value meets a preset condition, wavelet decomposition and frequency division multi-scale fusion are carried out on the polarization images after color correction, a fused image is generated, and the quality of the polarization images is effectively improved.

The invention discloses a video image color correction method and system based on artificial intelligence, and relates to the technical field of color correction, and the method comprises the steps: collecting video data, carrying out the image size normalization and denoising processing, and obtaining the preprocessed video frame sequence data; and extracting reflectivity data and illumination data by using a Retinex-Net deep learning model, calculating an illumination adjustment coefficient, carrying out pixel correction, and carrying out color temperature correction on the pixel data of the video frame subjected to pixel correction according to the illumination adjustment coefficient. According to the method, the preliminary color mapping matrix is generated through the color histogram matching method, the color distribution of the image can be optimized, the image is enabled to better conform to the color features of the target reference frame, the color temperature adjustment is performed on the image according to the illumination adjustment coefficient and the illumination threshold, the cold and warm tones of the video are enabled to be accurately adjusted, and the image quality is improved. Through successively carrying out pixel correction, color correction and local area correction, a stable color adjustment frame is provided, so that the video styles are consistent.

The invention discloses a two-stage network underwater image enhancement method based on color correction and multicolor space stretching, and the method comprises the steps: compensating a red channel and a blue channel through a green channel, obtaining a red channel correction image and a blue channel correction image, constructing an improved U-Net network, and outputting the weight mapping of the red channel and the blue channel; performing dot product multiplication on the red and blue channel correction images and the corresponding red and blue channel weight mapping, and splicing the dynamic compensation results of the red and blue channels and the green channel into an RGB space color correction image; the RGB space color correction image is converted into an HSI space and a Lab color space respectively, histogram stretching is carried out on the HSI space, the Lab color space and the RGB space respectively, finally, the three kinds of images subjected to histogram stretching are converted into the RGB color space respectively, and the three images converted into the RGB color space are spliced to obtain a comprehensive input image; and inputting the comprehensive input image into the improved U-Net network, training again, and outputting an enhanced image, so that the imaging resolution and the color correction effect of the underwater camera can be remarkably improved.

The invention provides a low-illumination image enhancement method based on adaptive brightness enhancement and high-fidelity color correction. Comprising the following steps: constructing a training data set, and performing data enhancement on paired low-illumination / normal-illumination images to obtain an image data set for training; designing a low-illumination image enhancement network based on adaptive brightness enhancement and high-fidelity color correction; designing a loss function for training the designed network; training the designed network by using the training data set; and inputting a to-be-measured low-illumination image into the trained network to generate a normal-illumination image. According to the method, the adaptive brightness improvement strategy and the color correction under the multi-color space constraint are utilized, the image brightness can be adaptively enhanced, the image color is subjected to high-fidelity correction, and a high-quality image with proper brightness and high-fidelity color is obtained; the problems of uneven brightness, color distortion, detail loss and the like of the low-illumination image are effectively solved.

The invention relates to the technical field of color correction, and discloses a publication printing color correction method and system based on deep learning, and the method achieves the precise correction and compensation of the color of a publication printed matter through a series of steps of cosine distance calculation, color-environment matching model, color deviation calculation, operation parameter adjustment and the like. The core of the method lies in that the accuracy of the printing color is effectively improved and the color quality of the printed matter is ensured to meet the expected standard through careful analysis and processing of the printing color by using the deep learning technology. The correction method provided by the invention has obvious advantages, not only can realize automatic color correction, reduce human intervention and improve the stability and consistency of correction, but also can optimize the operation parameters of the printing equipment, so that the printing equipment can keep a good working state in different printing environments, and the adaptability of the printing process is enhanced.

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 discloses a low-light image enhancement method based on a YUV color space, and relates to the technical field of image processing. According to the method, a color space illumination enhancement network (CSLNet is established, brightness (Y) and chrominance (U, V) information is separated, and brightness and chrominance channels are respectively subjected to targeted processing, so that the image brightness and details are improved, the problems of color distortion and noise amplification are avoided, the CSLNet makes full use of the advantages of a YUV color space, and the image quality is improved. Wherein the Y channel represents brightness information, the U and V channels represent chrominance information, brightness enhancement and color correction can be more accurately controlled through separation processing and the CSLNet, a color space enhancement module CSE Block (Color Space Enhancement Block) is introduced into the CSLNet, the CSE Block is fused with a U-shaped network structure and a multi-head self-attention mechanism, and optimization processing is specially carried out on chrominance channels U and V of a low-illumination image.

The invention discloses a marine litter intelligent identification and classification method based on a multispectral unmanned aerial vehicle remote sensing image, and relates to the technical field of image processing, and the method comprises the steps: collecting and preprocessing a remote sensing image of a preset sea area, constructing a color correction network, and carrying out the color correction of the preprocessed remote sensing image; multiband features and spectral indexes of enhanced garbage detection are obtained, and multispectral features are generated through multi-scale feature pyramid structure fusion; training a conditional generative adversarial network, inputting a remote sensing image and multispectral features, and performing pixel-by-pixel fusion on the enhanced remote sensing image and the remote sensing image after color correction to obtain a multiband fusion feature map; and constructing a lightweight detection network based on the OfficientDet-Lite, taking the multi-band fusion feature map as input, and outputting a bounding box and a garbage category of garbage by adopting a joint detection-classification architecture. According to the method, the problems of color distortion, contour fuzziness, small target missing detection and the like in marine litter identification are solved, and meanwhile, the real-time performance of marine litter identification and classification is ensured.

The invention discloses a tongue picture image processing system for traditional Chinese medicine health-care physique detection. The tongue picture image processing system comprises a tongue picture image acquisition module, a self-adaptive color correction module, an image enhancement module and a database and analysis module, the acquisition module automatically adjusts illumination conditions through an ambient light sensor to ensure the consistency and stability of images; the adaptive color correction module adopts color space conversion and a standard color library to automatically correct color distortion in the collected tongue picture image; the image enhancement module enhances the tongue coating, crack and tooth mark key features of the tongue picture through sharpening and contrast adjustment, the database and analysis module analyzes through a standardized tongue picture data set and big data, the processing precision of the tongue picture image is effectively improved, and a more scientific and accurate auxiliary tool is provided for traditional Chinese medicine constitution detection.

The invention discloses an atmosphere lamp user color calibration matching method. The method comprises the steps that K PCBA boards are installed on a lamp shell; measuring a color coordinate value and a luminous flux value of the atmosphere lamp; the color coordinate value and the luminous flux value of any atmosphere lamp serve as color correction data, and the pwm duty ratio is calculated; the controller controls the LED driver to drive the K atmosphere lamps to complete primary color correction; the PCBA board in any atmosphere lamp is taken out, and the actually measured luminous flux value of the PCBA board is measured; comparing the actually measured luminous flux value with a target luminous flux value to obtain a ratio r; increasing the target luminous flux value Yt by r times, and calculating a pwm target duty ratio in combination with color correction data; the controller controls the LED driver to drive the K atmosphere lamps to complete secondary color correction; and the color coordinate value and the luminous flux value of the atmosphere lamp after color correction are measured and compared with customer standards. The method has the advantages that assembly user color adjustment of multiple styles of atmosphere lamps is achieved, the matching degree of chromatic value control of the atmosphere lamps and real environment requirements is met, the production time cost is greatly saved, and the production efficiency is improved.

A method of generating a color mapping table for correcting color of a display panel, including: establishing a sampling point space, according to first color coordinates of a plurality of sampling points in a first color space; and performing steps of a preset processing on each of the plurality of sampling points, to obtain target color coordinates of the sampling point in the first color space, and generating the color mapping table according to the first color coordinates and the target color coordinates of the plurality of sampling points, where the steps of the preset processing on each of the plurality of sampling point include: taking the sampling point as a source sampling point, and determining a target sampling point corresponding to the source sampling point; and processing the target sampling point, to obtain the target color coordinates.

The invention relates to an underwater image enhancement method based on adaptive color equalization and multi-scale fusion, and belongs to the technical field of computer vision. The method comprises the following steps: firstly, carrying out adaptive color equalization on R, G and B channels of an original underwater image to reduce the difference among the R, G and B channels, and then carrying out color correction by using a white balance algorithm; then, constructing a gamma function for the image after color correction, performing brightness correction, and then performing Rayleigh stretching to obtain an underwater image with clear contrast; thirdly, performing sharpening based on a local variable coefficient on the image after gamma correction to obtain an underwater image with enhanced details, and stretching S and V channels of the processed underwater image to the whole interval; and finally, performing non-subsampled contourlet fusion on the underwater image after S and V stretching to obtain a final clear underwater image. According to the method, the color of the underwater image can be corrected, the contrast ratio of the underwater image is improved, and the details of the underwater image are enhanced.

The invention relates to the technical field of display, and provides a brightness and color correction method and system of an LED display screen, a temperature sensor is arranged on a substrate of the LED display screen, and the brightness and color correction method of the LED display screen comprises the steps of obtaining a current temperature value output by the temperature sensor; current adjustment parameters of the LED display screen are obtained according to the current temperature value and a preset parameter adjustment table describing the heat distribution adjustment parameter relation, and the current adjustment parameters are selected according to the heat distribution data corresponding to the current temperature value; and adjusting the brightness and color control data of different control areas of the LED display screen according to the current adjustment parameter so as to correct the brightness and color of the different control areas.