1796 results about "Color space" patented technology

The invention belongs to the technical field of industrial measurement, and discloses a pump machine metal shell size detection method based on image analysis, which comprises the following steps: synchronously acquiring image data of a pump machine metal shell in a visible light wave band and a near-infrared wave band; the contrast ratio of image data is optimized through a dynamic exposure control technology, and a high-dynamic-range multispectral image is obtained; three-dimensional geometric information of the pump machine metal shell is obtained, registration fusion is carried out on the three-dimensional geometric information and the high-dynamic-range multispectral image, a dense three-dimensional point cloud model with multispectral textures is constructed, and a multi-frequency heterodyne algorithm is adopted to process a high-reflection area in the dense three-dimensional point cloud model; the method comprises the following steps: collecting a multi-angle reflection image of a pump machine metal shell by rotating a linear polarizer, calculating a Stokes vector to extract a diffuse reflection component, carrying out diffuse reflection component constraint and brightness suppression on a high-reflection area based on a CIE-Lab color space, and outputting a dense three-dimensional point cloud model after texture enhancement; and the size detection precision of the pump machine metal shell is improved.

The invention discloses a video stream dynamic fragmentation encryption and block chain evidence storage method, and relates to the technical field of video content security, and the method comprises the steps: calculating a color histogram difference value and an optical flow vector change rate between adjacent frames of an input video, marking the difference value as a scene switching point when the difference value exceeds a preset threshold value, and storing the scene switching point; the method comprises the following steps: preliminarily dividing a video into a plurality of scene segments according to scene switching points, performing content complexity evaluation on the scene segments, calculating gray level co-occurrence matrix characteristics of each frame of image through texture density analysis, calculating edge complexity to extract the number and distribution of Canny edges, and performing motion vector statistics to analyze the size and direction of inter-frame object displacement. The change rate between adjacent pixels in the color space is measured according to the color change gradient; the video stream dynamic fragment encryption and block chain evidence storage method is suitable for video contents of different types and complexities, and has relatively high detection accuracy and robustness.

The invention discloses a pathological section virtual staining image generation method based on a diffusion model, and belongs to the technical field of medical image processing. The method comprises the following steps: firstly, passing through Hamp; e dyeing serves as a source image data set, and pathological section images subjected to IHC dyeing serve as a target image data set; preprocessing the source image, and converting the source image into a corresponding YUV color space; extracting a saliency map and a gradient map of the source image, and generating a weighted attention map; building a diffusion model by using a neural network, and inputting the preprocessed source image for fine tuning; repeatedly iterating until the loss function converges, and ending the training; and inputting a weighted attention map to perform reasoning of virtual dyeing. According to the method, weighted attention of a pathological section image is added in a reasoning part to serve as an image prompt input network, so that the cell structure of a finally generated image is controlled not to be changed, and meanwhile, color features of a target dyed image are reserved and emphasized; and the accuracy and the reconstruction effect of the generated virtual dyeing image are improved.

The invention discloses an image processing method applied to printed matter surface color difference detection, which relates to the technical field of image processing, and comprises the following steps: acquiring a digital image of a printed matter to be detected, performing illumination non-uniformity correction, and converting the corrected digital image into a CIELAB color space; performing region segmentation on the digital image based on double constraint conditions of color gradient and texture boundary to generate a detection region graph; feature parameters are extracted based on the detection area graph, and a feature data set is generated; establishing a dynamic reference model by utilizing process parameters and material characteristics of the printed matter; and calculating the distance between the feature data set and the dynamic reference model, identifying color difference regions and generating color difference scores, and screening and grading the color difference regions according to the color difference scores. According to the method, a multi-layer image pyramid and homomorphic filtering combined illumination correction technology is adopted, and an adaptive weight fusion mechanism based on image features is introduced, so that the illumination nonuniformity is effectively eliminated while the definition of printing details is kept.

The invention discloses an embedded power transmission line insulator chain defect identification method, system and device and a storage medium, and belongs to the technical field of defect identification, and the method comprises the steps: obtaining an insulator chain image, carrying out the insulator chain region positioning of the insulator chain image through color space change and image processing, and obtaining the coordinates of a target insulator chain; based on the target insulator chain coordinate, feature extraction is carried out on an insulator sheet, and an insulator sheet state vector is constructed; judging the state vector of the insulator sheet to obtain a suspected defective insulator list; calculating the insulator sheets in the suspected defect insulator list and detecting the surfaces of the insulator sheets to obtain the defect types and positions of the surfaces of the insulator sheets; and according to the defect type and position, the associated defect type code and description, generating structured power transmission line insulator defect report data, thereby improving the accuracy of defect identification, the fineness of defect classification and the practicability of a result report.

The invention discloses an artificial intelligence assisted makeup and makeup robot collaborative makeup method, and belongs to the technical field of artificial intelligence and robots. The method comprises the following steps: collecting user face data through a multi-modal sensor, extracting multi-scale features based on improved DenseNet and HRNet networks, and generating a three-dimensional face model; a high-fidelity virtual makeup effect is generated by using a conditional generative adversarial network in combination with user requirements, and real-time comparison and display are performed through an augmented reality interface; the makeup difference is quantified through multi-dimensional feature point mapping, multi-color space analysis and texture comparison, and correction suggestions are dynamically generated; operation is executed through the seven-degree-of-freedom redundant mechanical arm, a hybrid force control strategy and a visual monitoring system are integrated, and safe and accurate execution is ensured. The method solves the problems of uncontrollable traditional makeup effect, low execution precision and lack of dynamic feedback, has the characteristics of high adaptability, real-time response and zero-damage safety, and can be widely applied to the fields of family beauty, film and television makeup and special crowd nursing.

The invention discloses an image multi-modal feature extraction method, a ground feature classification and recognition method and a GIS image generation method. Comprising the following steps: firstly, extracting texture features from a target image by adopting a multi-directional statistical method fusing rotation invariant coding of a local binary pattern and a gray-level co-occurrence matrix, and extracting color features from the target image by adopting an LAB-HSV dual-color space collaborative analysis method to obtain features of different modes of the target image; and then according to the information values of the extracted color features and texture features, adjusting the weight ratio corresponding to the color features and the texture features so as to optimize the recognition precision of the classification model on complex ground features. And finally, according to a weight ratio corresponding to the color feature and the texture feature, performing weighted fusion on the color feature and the texture feature to obtain a corresponding multi-modal feature vector.

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 provides a high-resolution machine vision detection method and system for precise chromatic aberration detection and a medium, and the method comprises the steps: carrying out the calibration of a camera based on a camera calibration tool, and synchronously calibrating a light source parameter and a color parameter; setting shooting parameters based on the calibrated camera, obtaining a workpiece image in real time, preprocessing the workpiece image, mapping the preprocessed image to a standard color space from an original RGB space, and extracting color features of the preprocessed image; comparing the color feature with the color feature of the standard sample, calculating a color difference index, and comparing with the color difference index based on a set color difference threshold to obtain a detection result; by calibrating various parameters of the camera, the visual detection precision is ensured, and by performing color space mapping on the workpiece image and analyzing the difference between the color difference index of the color feature and the color difference threshold value, the color abnormal area is accurately analyzed, and the detection precision of the abnormal color is improved.

The invention relates to a color calibration method and system based on a display driving chip, and the method comprises the following steps: carrying out the sub-pixel spectrum reconstruction of an original spectrum data stream based on a color gamut boundary vector group, and obtaining a sub-pixel spectrum matrix; performing white point chromaticity estimation on the sub-pixel spectrum matrix to obtain white point chromaticity coordinates; performing gamma curve correction on the sub-pixel spectrum matrix based on the white point chromaticity coordinates to obtain a gamma correction coefficient matrix; performing color space conversion on the gamma correction coefficient matrix through a preset three-dimensional lookup table to obtain color control parameters, and inputting the color control parameters into a preset display driving chip to drive sub-pixels of each pixel unit in the display panel to emit light to obtain color calibration display output, the defects of a traditional display driving chip in the aspect of color calibration are overcome, and the defects are mainly embodied in the technical problems that color differences between different display panels cannot be effectively processed and the color drift phenomenon occurs along with time lapse.

The invention provides a low-illumination image enhancement method, and belongs to the technical field of image processing. The method comprises the following steps: converting an original RGB image into an HSV color space to obtain a chroma channel component, a saturation channel component and a brightness channel component; enhancing the saturation channel component and the brightness channel component to obtain a saturation channel enhanced component and a brightness channel enhanced component; fusing the saturation channel enhancement component, the brightness channel enhancement component and the chroma channel component to obtain a synthesized RGB image; and performing noise reduction processing on the synthesized RGB image to obtain an enhanced RGB image. Through the method provided by the invention, the brightness balance, the detail retention degree and the color naturalness of the output image are remarkably improved; therefore, the problems of overexposure, noise amplification, detail loss and the like which are easily caused by unbalanced brightness distribution under low illumination are solved.

The invention relates to the technical field of paper marking image acquisition optimization, in particular to an intelligent paper marking image acquisition system and method based on AI and terminal equipment. An original image is acquired through an image acquisition unit, and a color temperature distribution thermodynamic diagram is generated by a light source analysis unit through a convolutional neural network; the dynamic compensation unit divides compensation grids and executes exposure compensation, the self-adaptive correction unit outputs a target image through bilinear interpolation and color space conversion, and the quality evaluation unit compares a positioning mark color gamut value to trigger a grading re-shooting mechanism. The problems of insufficient complex illumination adaptability and poor image quality stability in the prior art are solved, highlight-shadow intelligent compensation, color space adaptive conversion and quality closed-loop control of the answer sheet image are realized through AI-driven multi-unit cooperation, the full-filling point recognition accuracy and the image qualification rate are improved, and the image quality is improved. And the high-efficiency examination paper marking requirement of large-scale examination is met.

The invention discloses an image digitization method, system and device and a storage medium, and relates to the field of data processing. The method comprises the following steps: acquiring spectral data, three-dimensional shape data and material reflectivity parameters of a target image; based on the three-dimensional shape data, analyzing surface deformation characteristics of the target image, and generating a geometric distortion distribution diagram; generating geometric correction parameters according to the surface deformation features, and reconstructing pixel positions of the geometric distortion distribution diagram to obtain reconstructed image data; and mapping the spectral data to a preset color space, matching a pre-established color database, and adjusting the color component of the reconstructed image data to obtain corrected image data. By implementing the technical scheme provided by the invention, a set of traditional Chinese painting digitization technical system considering high-precision acquisition, intelligent correction and efficient management can be developed.

The invention relates to the technical field of medical treatment, and particularly discloses an intelligent wound measuring and recording system and method.The method comprises the steps that multi-dimensional image data and three-dimensional depth information of a wound area are synchronously collected, hierarchical space division and nonlinear feature coding are adopted for preprocessing the multi-dimensional image data, and wound basic information is generated; constructing a wound measurement model based on the basic information of the wound, extracting wound features with space-time relevance, and extracting a wound contour of multi-dimensional image data by using an improved U-Net architecture in combination with an HSV color space and morphological gradient method; according to the method, the multi-dimensional image data and the three-dimensional depth information are synchronously acquired, and the advanced image processing technology is combined, so that the accurate extraction of the wound contour is realized, and the problem that wound details cannot be accurately identified and measured in a traditional method is solved; based on an ICP registration algorithm and a three-dimensional point cloud reconstruction technology, submillimeter-level wound volume measurement is realized, and the wound volume evaluation precision is improved.

The invention provides a carton printing quality detection method and system based on industrial vision, and belongs to the field of printing quality detection.The carton printing quality detection method comprises the steps that S1, a carton printing image is obtained, reflective interference suppression is conducted based on color space conversion, printing feature enhancement is conducted, and a to-be-detected image is obtained; s2, performing printing quality detection on the to-be-detected image, and determining defect area distribution characteristics and defect types of carton printing; s3, quality grading is conducted on the carton printing quality based on the defect area distribution characteristics of carton printing, and repairing suggestions are generated; s4, performing correlation analysis on the carton printing quality grade and the repair suggestion and production process data of carton printing, generating a quality tracing record, and storing the quality tracing record in a quality management database; and S5, optimizing the carton printing quality detection method. According to the invention, the printing defects can be quickly identified in a high-speed production line environment, and the whole-process management and optimization of the printing quality problem can be realized.

The invention discloses a remote photoplethysmography (rPPG) signal processing method and system based on a long and short term space-time convolution network, and belongs to the crossing field of biomedical signal processing and computer vision. In order to solve the problem of signal distortion caused by illumination fluctuation, motion artifacts and skin color differences, the method constructs a multi-scale space-time modeling framework: extracting local space-time features of transient changes of facial capillaries by adopting a 3D convolutional network, capturing long-range periodic features of heart rate rhythm in combination with a 1D expansion convolutional network, and establishing a multi-scale space-time modeling framework; the spatial-temporal characteristics are dynamically fused through the self-attention weight and the gating residual structure, and the anti-interference capability is improved. In the preprocessing stage, a face area is positioned through MTCNN, motion artifacts are compensated by using an optical flow equation, and signal purity is enhanced by combining a skin color mask and a YUV color space. The system adopts a deep separable convolution and parallel acceleration strategy to realize light weight, and optimizes the network through time domain MSE loss and frequency domain KL divergence. Experiments show that the phase error of the method is reduced by 40% in a dynamic scene, the signal amplitude of a deep skin color group is improved by 60%, the method is suitable for non-contact health monitoring equipment, and the robustness and the measurement precision of the rPPG technology in a complex environment are remarkably improved.

The invention discloses an insulator space attitude positioning and three-dimensional reconstruction method based on Realsense. The method comprises the following steps: acquiring an RGB image and a depth image of an insulator through a Realsense depth camera; identifying and segmenting the RGB image of the insulator by using a Mask RCNN (Regional Convolutional Neural Network) to obtain a mask image of the insulator; dividing and repairing the edge contour of the mask pattern according to the region of the HSV color space; three-dimensional coordinates of key points of the insulator are extracted, a space linear equation of the central axis is fitted, and the space position and the attitude angle of the insulator are determined; repairing the region depth map of the filled insulator by using an algorithm of symmetric scanning filling and scanning filtering along an axis to obtain a complete and accurate depth value; and fusing the plane information extracted from the mask image and the accurate depth information of the repaired depth image to realize three-dimensional reconstruction of the insulator. According to the method, the depth map is accurately repaired and filled according to the depth information of the symmetrical positions of the space symmetrical parts such as the insulator, the problems of depth deficiency and inaccuracy of the Realsense depth map at the edge of the object to be detected are solved to a certain extent, the RMSE and PSMR of the regional depth map are improved, and the effect of enhancing the three-dimensional modeling accuracy of the insulator is achieved.

The invention discloses a dynamic object monitoring method and device, equipment and a storage medium, and relates to the technical field of image recognition, and the method comprises the steps: obtaining a plurality of frames of HSV images of a target scene; for any frame of HSV image, calculating an optical flow field of the HSV image based on an image difference between the HSV image and an adjacent frame of HSV image; and determining a dynamic object of the target scene according to the displacement of each pixel point in the optical flow field. The HSV color space and the optical flow algorithm are combined to carry out image processing and dynamic object detection, the situation that the object recognition precision is reduced due to the fact that a traditional RGB color space is sensitive to illumination change is avoided, expensive hardware equipment or a complex illumination correction technology is adopted to overcome the influence of the illumination change, and the object recognition precision is improved. The dynamic object can be monitored stably and accurately at low cost in an environment with large illumination condition changes.

The invention relates to a light-weight low-light image enhancement method based on illumination iterative adjustment, and the method comprises the steps: carrying out the HVI color space transformation of a to-be-processed image, obtaining an HVI image, separating an illumination intensity component, obtaining an illumination intensity feature map, and carrying out the convolution processing of the HVI image, and obtaining a shallow feature map; feature fusion and brightness adjustment are carried out on the illumination intensity feature map and the shallow layer feature map to obtain a depth feature map, up-sampling is carried out step by step through two layers of decoders to recover the size, channel adjustment is carried out on each layer of decoder by adopting convolution of a specified size, and up-sampling is realized through bilinear interpolation; a Mama-based structure refining module is introduced into the tail end of each layer of decoder to improve the structure restoration capability, the number of channels of HVI features output by the last layer of decoder is adjusted to a specified value through a convolution of a specified size, residual connection is carried out on the HVI features and an initial HVI image, an enhanced HVI image is obtained, and a restored image is obtained through inverse HVI transformation. Adaptive enhancement is carried out under various low light conditions, multi-directional spatial context aggregation is realized, and texture representation is improved.

The invention discloses a wafer probe mark accurate detection method based on deep learning, and belongs to the field of wafer probe mark detection, and the method comprises the steps: constructing a pin mark image denoising preprocessing network, employing a small target feature protection and enhancement strategy based on HSV color space and local contrast joint adjustment, and carrying out the recognition of a small target feature; denoising and contrast optimization are carried out on the needle mark image; a multi-scene training sample is generated through mosaic splicing and mix fusion; a dense small target enhancement module is introduced into the backbone network to enhance needle mark feature expression, and a multi-scale feature fusion module is arranged in the neck network to extract full-scale features; and establishing an anchor frame optimization system adaptive to the minimum needle mark target, and adopting an optimizer and learning rate collaborative optimization training strategy to realize model adaptive convergence. According to the method, high-precision detection and robust identification of the wafer probe mark can be realized under a complex background, and the detection accuracy and stability are remarkably improved.

The invention discloses an unmanned aerial vehicle-mounted GNSSINS visual combination fire detection method, system and device and a medium, belongs to the technical field of unmanned aerial vehicles, and aims to solve the technical problem of how to improve the positioning precision and fire recognition capability of an unmanned aerial vehicle in a complex environment. Setting a dynamic temperature threshold value, and calibrating the threshold value in real time in combination with the environment temperature; identifying a fire source diffusion trend by adopting a temperature gradient analysis method, and excluding isolated high-temperature points; an image is collected through an RGB camera and converted into an HSV color space, flame features are extracted, and the flame shape is detected in real time through a YOLOv5 model; detecting a smoke area based on a U-Net segmentation network, outputting a binary mask, carrying out space-time alignment on the binary mask and the wind speed and wind direction of the meteorological data, and predicting the fire spreading direction; thermal imaging, visible light and smoke detection results are fused through the D-S evidence theory, and when the confidence coefficients of the thermal imaging, visible light and smoke detection results are all larger than 80%, it is judged that a fire exists.

The invention discloses an adversarial sample generation method based on a partially perceptible patch, and the method enables a specific region of a target image to be visually close to the characteristics of a natural image through the optimization disturbance of the specific region of the target image, maximizes the interference to a target detection model, and improves the attack effectiveness while maintaining the concealment. The invention further provides a device of the adversarial sample generation method based on the partially perceptible patch. According to the method, color space transformation and random deformation are combined, the adaptability of the adversarial sample in different environments is further enhanced, and the method has higher robustness and universality in practical application.

The invention discloses a color temperature correction method and device of a liquid crystal display system and a storage medium. The method comprises the following steps: determining tristimulus values of original white pixel points of the system in a CIE 1931XYZ color space according to normalized spectral power distribution of a white light backlight source of the liquid crystal display system and a transmittance spectrum of a three-primary color filter of a color film; determining a target RGB color space and a target color temperature, and constructing a color space conversion matrix from the target color space to an XYZ color space; converting the tristimulus values of the original white pixel points of the system in the XYZ color space into the three-primary color channel values of the target white pixel points under the target color temperature in the target color space by using the inverse matrix of the color space conversion matrix; and driving the system to display white according to the three-primary color channel value of the target white pixel point. According to the scheme, the color temperature correction of the liquid crystal display system is realized through a method of combining measurement and theoretical calculation, and the color temperature correction efficiency is improved.

A method for analyzing a sample includes introducing the sample into a vessel including a cation-exchange resin, where the cation-exchange resin has a high affinity to one or more interferent species in the sample, directing the sample through the cation-exchange resin to thereby capture one or more interference species in the cation-exchange resin to purify the sample, and releasing the purified sample from the vessel. Additionally, a method for analyzing a sample includes receiving an image depicting a diagnostic test region and a color reference chart, converting a test region image portion of the image to an analytical color space, determining first and second coordinate values characterizing the test region image portion in the analytical color space, and providing a quantitative measurement of an analyte of interest in the sample based on the first and / or second coordinate values and a predictive formula based on the color reference chart.

The invention discloses a tea leaf fermentation degree determination method with image recognition, which comprises the following steps: shooting tea leaves through a high-definition camera and a camera in different tea leaf processing stages according to different tea leaves to obtain high-definition images of the tea leaves, preprocessing the collected tea leaf images, and determining the fermentation degree of the tea leaves according to the preprocessed tea leaf images. Through denoising, contrast enhancement and color correction technologies, an acquired image is preprocessed, the image is converted into a proper color space for extracting color features in the tea image, and dominant hue and color distribution on the surface of tea are analyzed. The invention relates to the technical field of tea leaf detection, and the method can realize non-destructive, real-time and accurate tea leaf fermentation degree monitoring, and automatically judges the fermentation state of tea leaves by shooting tea leaf images and analyzing visual features of colors, forms and textures of the tea leaves in combination with a machine learning algorithm. Therefore, the automation level and the production efficiency of the tea processing process are improved.

The invention relates to the technical field of computer vision and image processing, and discloses a low-light image enhancement method based on a conditional diffusion model and an attention mechanism. The method comprises the following steps: converting an input low-light image and a normal-light image from an RGB color space into an HVI color space, and decomposing the HVI color space; obtaining a brightness component and a horizontal and vertical component; performing iterative brightness recovery on the obtained brightness component through a conditional diffusion model to obtain an enhanced brightness component; performing color preservation and local and global detail enhancement on the obtained horizontal and vertical components through a residual attention module to obtain enhanced horizontal and vertical components; and synthesizing the obtained enhanced components into an HVI image, converting the HVI image back to an RGB image, and finally obtaining an enhanced image. According to the method, the problem that an enhancement algorithm in the prior art is not easy to obtain a local detail which is fully reserved, the color is kept undistorted, and the generalization ability is improved to adapt to images under different conditions is solved.

The invention discloses a combined denoising and demosaicing method for a low-exposure original image under low light, and the method is divided into two stages: in the first stage, introducing a high and low frequency fusion module based on wavelet transform, and generating relatively clean pseudo long exposure data to reduce noise; and in the second stage, through a color space attention subnet, image reconstruction is carried out by using the pseudo long exposure data generated in the first stage, the color is recovered, and texture details are enhanced. In addition, the invention also provides a wavelet loss function to balance detail recovery and noise suppression, and discusses the influence of three different training strategies on the performance of the model. Experimental results show that the method provided by the invention is superior to most advanced algorithms in the aspects of quantitative indexes, color accuracy, detail retention and the like.

The invention discloses an intelligent management system and method applied to clothing flexible customization, and relates to the technical field of clothing intelligent manufacturing, and the system comprises a multi-source color data acquisition module, a color space preprocessing module, a dynamic color space mapping module, an edge real-time calibration module, an equipment state monitoring module and a full-process data closed-loop module. Wherein the multi-source color data acquisition module constructs a cross-device color reference library, the color space preprocessing module standardizes color data and unifies a color gamut, the dynamic color space mapping module generates and optimizes a dynamic conversion matrix, and the edge real-time calibration module updates device parameters in combination with fabric characteristics. The equipment state monitoring module constructs a correlation model to analyze the equipment state and color deviation, and the full-process data closed-loop module stores data through a block chain and optimizes an algorithm. The system realizes a complete process from fabric color data acquisition, cross-device mapping calibration, device state monitoring to full-process data closed-loop management through cooperation of the modules.

The invention discloses a skin image super-resolution conversion system and a skin image super-resolution conversion method based on deep learning, and relates to the technical field of image super-resolution conversion, and the system evaluates whether illumination is qualified or not by collecting skin near-illumination and analyzing illumination conditions of the skin near-illumination, establishing a light environment analysis model and calculating a light influence coefficient. The method comprises the following steps: preprocessing qualified images, including color space conversion, histogram equalization, edge detection and denoising; thirdly, evaluating the quality of image pixels, calculating a plurality of pixel features, judging whether the pixel features conform to the standard or not, and performing optimization through strategies such as sharpening and de-noising; a multi-scale feature extraction network and a self-attention mechanism are adopted to extract image features, a cross-modal mapping model is constructed, super-resolution conversion is performed through a generative adversarial network, and image details and definition are improved. And finally, monitoring the quality of the super-resolution image, evaluating the conversion effect, and adjusting the processing strategy according to the quality coefficient to ensure that the output image meets the medical diagnosis requirement.

The invention provides an underwater multi-color space image enhancement method based on depth information guidance, which is characterized in that a model of the method is based on a training model framework capable of utilizing physical priori knowledge and multi-color information, and comprises a depth information generation module, a multi-color space feature fusion module and a depth enhancement module, the relation between a depth image and an underwater scene is enhanced through attention estimation, scene reconstruction training and fusion of multi-color information, so that insensitivity of a single RGB color space to brightness and saturation image attributes is made up; the feature representation capability is improved, and the scene adaptability of the model is enhanced.