282 results about "Color consistency" patented technology

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 relates to the technical field of optical detection, in particular to a glass bottle surface spraying quality detection method which comprises the following steps: under a specified wavelength, acquiring target incident light by adjusting a polarization angle, irradiating a glass bottle spraying layer, collecting a transmission spectrum signal to calculate parallel and vertical polarization transmissivity and ratio of the spraying layer, and generating polarization transmissivity. According to the method, the light with the polarization angle irradiates the spraying layer, the polarization component of the transmission light is obtained, the thickness uniformity and the internal structure are analyzed, weak areas, particle flaws and excessive spraying of the spraying layer are recognized, the defect detection precision is improved, multi-angle transmissivity data are collected, the change rate is calculated, and color deviation and thickness abnormity are accurately positioned; through cross analysis of multiple optical parameters, evaluation of adhesive force, curing quality and color consistency is more detailed, spraying abnormity is classified, multiple detection means act synergistically, interference such as environment illumination and material reflectivity is reduced, and the detection accuracy of spraying quality is improved.

The invention relates to the technical field of image recognition, in particular to a bridge pier underwater structure image intelligent analysis method and system, and the method comprises the following steps: obtaining RGB channel values of all pixels of a bridge underwater structure image region, converting a main color temperature, generating a color temperature map, dividing the region in a vertical direction, extracting a main color temperature track, and carrying out the linear fitting. And comparing the residual error with a continuity threshold, and marking a color temperature continuous section. According to the method, a two-dimensional map is constructed through fusion of main color temperature and space coordinates, spatial distribution expression of a structural region is enhanced, a stable region is extracted by combining residual and continuity discrimination, interval judgment and RGB finishing are introduced into gray scale lifting, image brightness balance and color consistency are enhanced, a gray scale trend model is constructed through radial chain grouping, and light attenuation interference stripping is achieved. Feature point continuous frame tracking is combined with disturbance vector mapping, structure dynamic changes are captured, abnormal behaviors are recognized through cross analysis of tension differences and response terms, and efficient recognition and interference isolation under multi-dimensional feature fusion are achieved.

The invention provides a cross-platform content compatibility method and system based on a virtual shooting LED display screen. The method comprises the following steps: processing an irrelevant data content stream of a content stream of a heterogeneous content source to generate an intermediate data stream; constructing a virtual logic display space according to the resolution parameter of the intermediate data stream and the curvature data of the physical screen database; performing dynamic resource allocation based on the control point grid of the virtual logic display space, and generating a rendering task queue; loading a three-dimensional lookup table to perform forward color gamut conversion according to the rendering task queue and a physical screen color configuration file, and adjusting HSL offset according to collected real-time chroma data to generate frame cache data; and determining a delay compensation value based on the frame buffer data and the PTP clock signal, and inserting a corresponding time offset in an output queue through the delay compensation value to generate a multi-screen output signal. According to the scheme, the rendering accuracy, the color consistency and the display stability in the virtual shooting scene can be remarkably improved.

The invention relates to a low-illumination underwater image enhancement method based on double-domain adaptive collaboration, and the method employs a multi-scale mixed attention mechanism to collaboratively integrate the spatial domain and frequency domain information of an image, and obtains the key feature information of the image. And on the basis of the obtained image key feature information, adaptive parametric convolution is used, convolution kernel parameters are dynamically adjusted by using a multi-branch convolution structure, and image dynamic key information is obtained. And a dynamic content perception Markov discriminator is used to cooperatively perceive space and frequency characteristics, and the authenticity of the enhanced image is discriminated, so that the fidelity of the texture is improved. And finally, optimizing the generator and the discriminator by using adversarial training, and finally outputting an enhanced underwater image by using the optimized generator. The method can be embedded into an underwater robot platform, image quality enhancement is achieved with fewer computing resources in a low-illumination complex water environment, image definition, contrast and color consistency are improved, the enhanced image is closer to human eye visual perception, and the visual perception capability and operation reliability of an underwater robot are remarkably improved.

A computer that includes a processor and a memory, the memory including instructions executable by the processor to determine a first prediction with a machine learning system based on receiving a first image from a first camera and determine a second prediction with a machine learning system based on receiving a second image from a second camera. When the first prediction does not equal the second prediction within a user determined tolerance, determine color consistency based on comparing pixel values from the first image with a threshold determined based on previously determined pixel values, determine color correction parameters by determining pixel statistics based on pixel values from the first image to include in an image signal processing system and apply the color correction parameters to a third image from the first camera by receiving the third image at the image signal processing system.

The invention provides a printing color consistency automatic calibration and management system and method, and the method comprises the steps: collecting a current environment temperature and humidity ink matching parameter density curve data dot increase compensation parameter in real time through a state detection device, and forming a current state data set; performing similarity analysis on the reference data set and the current state data set by adopting a matching degree evaluation algorithm, and judging a relationship between a matching degree numerical value and a preset threshold value; adjusting and correcting the calibration reference value and the color control parameter in the reference data set according to the offset compensation set to obtain a compensated and adjusted parameter set; on the basis of the compensated and adjusted parameter set, generating update code data by combining a current time identifier and an operator identifier, and forming new two-dimensional code information; and outputting the new two-dimensional code information to a subsequent work order carrier by adopting a code generation module, and establishing a parameter transmission tracing chain.

The invention relates to the technical field of image processing and cultural relic protection, in particular to a cultural relic image virtual restoration method based on texture reconstruction and color correction, and the method comprises the following steps: S1, obtaining a digital image of the surface of a to-be-restored cultural relic, and carrying out the denoising and brightness equalization processing of the digital image; s2, identifying and segmenting a damaged area in the image; s3, selecting an optimal sample block which is most matched with the structure of the region to be filled; s4, performing adaptive affine transformation on the optimal sample block to generate a reconstructed texture block; s5, carrying out linear transformation to obtain a repaired texture block after color correction; and S6, seamlessly embedding the repaired texture block after color correction into the damaged area. According to the method, the texture direction alignment and the color distribution correction are combined, so that the consistency of the damaged area and the surrounding image in structure and color is realized, and the naturalness and integrity of cultural relic image restoration are remarkably 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.

The invention relates to the technical field of computer graphics and deep learning, in particular to a simulation bait automatic coloring method and system based on a 3D model. The method comprises the following steps: acquiring an uncolored 3D model and a reference image, and generating standard data through analysis verification, curvature grid division and image compliance detection; performing color conversion, texture enhancement and multi-scale downsampling on the compliant image to construct an image pyramid; performing multi-level feature extraction and adversarial training optimization based on a pre-trained convolutional neural network and a generative adversarial network, and generating an enhanced color texture map; performing UV expansion, color mapping and normal mapping fusion in combination with the model topology, and constructing an intermediate model with physical rendering attributes; and batch color consistency verification is realized through color histogram comparison, adaptive threshold segmentation and iteration parameter adjustment, and a standard model group is generated. According to the invention, efficient and highly realistic automatic coloring of the simulated bait is realized, and color consistency and rendering quality in batch production are guaranteed.

The invention discloses a text-to-three-dimensional object (3D) generation method based on dual color consistency matching, and aims to solve the problem of 3D asset quality reduction caused by multi-view color inconsistency in the prior art. According to the method, in each training iteration, a group of multi-view images are firstly rendered from a 3D model, and a high-fidelity forward view is specified as a color reference; subsequently, optimization is carried out through a double matching mechanism: 1, matching among images: aligning colors of other views to the reference view by using depth semantic features, thereby breaking circulation of error accumulation; 2, image internal matching: in a single image, color and geometric smoothness in the same semantic region are standardized by improving total variation loss; through the dual mechanism, accumulation and propagation of unnatural color distortion are effectively prevented, color inconsistency is reduced, and the visual fidelity and color consistency of the generated 3D model are remarkably improved.

The invention relates to the technical field of scene prediction, in particular to a vehicle scene prediction method, and provides the following scheme: a vehicle-mounted end collects vehicle end data and vehicle control data, a cloud end receives and generates a predicted scene video stream, and a cabin end performs color adjustment and image quality evaluation after receiving the predicted scene video stream. And adjusting a display queue according to an evaluation result and generating vehicle control data. In the color adjustment process, motion compensation, color distortion processing and secondary local compensation are performed based on the vehicle control data so as to improve the color consistency and space-time coherence of the prediction frame. Image quality evaluation is based on multi-color space feature extraction and a nonlinear regression network, quality scores are output in real time, and remote control decisions are guided. And the cloud end generates a continuous and high-quality prediction scene video stream by combining light stream prediction with self-motion compensation and shielding repair, so that low-delay and high-stability remote vehicle control is realized.

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.

The invention relates to the field of computer vision, and discloses a laser radar and camera joint calibration method, electronic equipment and a storage medium. The method comprises the following steps: acquiring point cloud data of a plurality of laser radars and image data of a camera; performing motion distortion compensation on the point cloud data to generate a global point cloud map; converting the global point cloud map into a plurality of three-dimensional Gaussian voxels with color attributes and covariance attributes; minimizing the first loss function by jointly optimizing the attribute of the three-dimensional Gaussian voxel and the external parameter of the camera so as to complete the calibration of the external parameter of the camera; wherein the first loss function comprises a color consistency constraint term and a rendering error term, and the color consistency constraint term is used for constraining that observed color attributes of a single three-dimensional Gaussian voxel at multiple moments and multiple visual angles are kept consistent; the rendering error term is a pixel color error between a projection image generated by the three-dimensional Gaussian voxel and image data of the camera. Therefore, the precision of external parameter calibration in a dynamic scene is improved.

The invention provides a high-dispersity polypropylene primary color master batch as well as a preparation method and application thereof, and relates to the technical field of polypropylene, the high-dispersity polypropylene primary color master batch comprises the following components in parts by weight: 1-20 parts of pigment, 55-74 parts of polypropylene, 1-3 parts of nano titanium dioxide, 1-3 parts of nano silver, 1-3 parts of tungsten trioxide and 2-5 parts of dispersing agent. 1-3 parts of a phosphate heat stabilizer and 0.5-2 parts of an antioxidant. Through the composite synergistic effect of nano titanium dioxide and nano silver and other nano materials, the uniform dispersion performance of the polypropylene color master batch in a base material is improved, pigment agglomeration is effectively prevented, and the color consistency of a final product is ensured; functional components such as tungsten trioxide and nano zirconium oxide are introduced, so that the heat resistance and the ultraviolet aging resistance of the polypropylene product are enhanced, and the polypropylene keeps stable color under long-term outdoor use conditions and is not easy to fade and degrade.

The invention particularly relates to a product integral forming automatic optimization method applied to 3D printing, which comprises the following steps: model importing and processing: slicing a three-dimensional model, including comprehensively analyzing a geometric coefficient and a visual coefficient to obtain an evaluation coefficient, and judging a non-manifold structure according to the evaluation coefficient; preparing materials; calibrating the equipment; printing a product; and post-processing. According to the method, the geometric coefficient and the visual coefficient are comprehensively analyzed to obtain the evaluation coefficient, and the non-manifold structure is judged according to the evaluation coefficient; in geometric coefficient calculation, the number of vertex edges of the model is meticulously counted and analyzed, so that the local abnormal condition of the topological structure of the model can be quantified; in the aspect of visual coefficient, the color consistency of the model is considered; compared with a single detection means, the multi-dimensional evaluation mode can more comprehensively and accurately detect the potential non-manifold structure in the model, and discover the problem possibly causing printing failure or quality reduction in advance, thereby greatly improving the printing success rate.

The invention provides a bidirectional decoupling translation unsupervised defogging network with feature and pixel comparison representation, decoupling modeling of haze and clear content in an image is realized through a feature and pixel comparison mechanism, and paired clear images do not need to be supervised and trained. The whole network is composed of five key modules: 1) a content recovery branch adopts a multi-scale residual structure to recover image details and textures, and cooperates with a semantic enhancement and color correction module to improve visual quality; 2) the parameter estimation branch estimates a transmissivity map and atmospheric light based on an atmospheric scattering model, and improves the prediction accuracy through context modeling; 3) a feature comparison learning module strengthens the discrimination of clear and fog features; 4) introducing closed-loop consistency by the reverse fogging structure based on reverse physical modeling, and providing an unsupervised training signal; and 5) a pixel comparison mechanism is used for constructing a comparison path by using the direct / indirect defogged image, the reverse fog-added image and the original image, and the network discrimination capability and generalization performance are enhanced. The experiment is carried out on a real natural fog image data set, the result shows that the proposed method is superior to the existing defogging method in the aspects of structural fidelity, detail reduction and color consistency, and the practicability of the method in a complex environment is verified.

The invention provides a physical guidance-based sparse coding-driven underwater image enhancement method, a program, equipment and a storage medium. The method comprises the following steps: constructing a physical guidance preprocessing method based on a Jaffe-McGammy model, and adaptively estimating an underwater transmission image and background light to realize preliminary degradation recovery; elastic regularization sparse coding is adopted, and through sparsity and smoothness combined constraint, salient structure information such as edges and textures of the image is effectively extracted, so that the image definition and the detail recovery capability are enhanced; cross-channel attention fusion is adopted, a multi-scale context modeling and channel interaction mechanism is introduced, and a physical degradation graph is combined for guidance, so that color consistency and semantic expression integrity between channels are remarkably improved, and the problems of color shift and information loss in an existing method are solved. According to the method, through joint optimization of multiple loss functions, the constructed underwater image enhancement network keeps the illumination accuracy, texture continuity and structural integrity of the image in multiple dimensions at the same time.

The invention discloses an image processing method and device and a storage medium, and belongs to the technical field of shooting. The method is applied to the electronic equipment comprising a plurality of cameras, the plurality of cameras comprise a reference camera, and the method comprises the following steps: under the condition that a first camera is called for shooting, determining a first white point of a current frame shot by the first camera and a first environment color temperature of a shooting environment corresponding to the current frame; according to an environment color temperature difference between the first environment color temperature and a second environment color temperature of a shooting environment corresponding to a memory frame of a reference camera, carrying out fusion processing on white point information of the first white point and white point information of a second white point of the memory frame to obtain white point information of a third white point; and performing white balance correction on the current frame according to the white point information of the third white point. Therefore, the color difference of the images shot by other cameras and the reference camera in the same scene and the visual difference caused by the color difference can be reduced, and the color consistency of multi-camera shooting in the same scene is improved.

The invention relates to the technical field of video generation, in particular to a video frame generation system and display equipment based on texture reconstruction and trajectory optimization, and the system comprises a motion breakpoint detection module, a trajectory interpolation frame generation module, a texture anomaly positioning module, a texture interpolation reconstruction module and a video frame synthesis module. According to the method, an edge feature map is generated through edge detection and non-maximum suppression, the boundary recognition precision is improved, noise is suppressed, candidate regions are divided through region growth, the local feature extraction efficiency is enhanced, global redundancy is reduced, three-frame optical flow analysis captures trajectory breakpoints, and motion vector correction path deviation is predicted in combination with a Lucas-Kanade method. Color consistency verification optimizes interpolation to reduce cross-frame abrupt change; 64 * 64 grids and a gray level co-occurrence matrix locate texture abnormity; five-frame similarity strengthens time sequence stability; Gabor filtering extracts a main direction and combines an adjacent frame reconstruction texture direction to ensure consistency; and trajectory smoothness and visual consistency are optimized through multi-frame cooperation and layered detection.

The invention discloses a low-illumination image enhancement method combining multi-channel parallel attention and cross fusion, and relates to the field of photoelectric display. In order to solve the problems of high model complexity, brightness and color gamut information coupling, artifact fusion and the like in low-illumination image enhancement, the invention provides a lightweight low-illumination image enhancement network, brightness and chrominance information are separated in a YCbCr color space, brightness structure features are extracted through a lightweight denoising module and a multi-head attention mechanism, and a low-illumination image is obtained. And a multi-channel parallel attention module (MCPA) is designed to enhance the chromaticity context modeling capability. According to the invention, a brightness-guided cross fusion module (LCA) is introduced, and cooperative enhancement of the structure and the color is realized through joint channel-space attention optimization. According to the method, a relatively high image quality index is obtained on multi-class public low-illumination data, meanwhile, the parameter quantity and the calculation quantity are remarkably reduced, and the method is suitable for edge device deployment. According to the method, color cast and fusion artifacts can be effectively inhibited, and the consistency of structural details and colors is improved.

The invention discloses a concrete apparent quality intelligent detection and evaluation method and system based on an unmanned aerial vehicle and deep learning, and the method comprises the following steps: S1, carrying out the high-resolution image collection of a target concrete member through employing an unmanned aerial vehicle matrix photographing technology, and carrying out the preprocessing of the collected image, and obtaining a concrete panorama; s2, inputting the preprocessed concrete panorama into an image segmentation network for defect segmentation processing; s3, calculating different indexes based on the defect segmentation result by adopting a digital image processing algorithm to quantitatively evaluate the concrete apparent defects; s4, performing illumination and color preprocessing on the concrete panorama to generate a chromatic aberration thermodynamic diagram, and calculating an internal color consistency index and a design color deviation index to perform chromatic aberration quantitative analysis; and S5, summarizing the quantitative indexes obtained in the steps S3 and S4, grading the appearance quality of the concrete according to a preset quality standard, and generating a detailed detection and evaluation report.

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

The invention relates to the technical field of image enhancement, in particular to a damaged archive restoration method and device, and the method comprises the following steps: selecting a group of damaged archive images and undamaged archive images, and collecting a color histogram of the undamaged archive images to obtain color histogram data; and based on the color histogram data, adjusting a color histogram of the damaged archive image through a histogram matching technology, matching histogram distribution of the undamaged archive image, and generating a color correction image. According to the method, through matching correction of the image color histograms of the damaged archives and the undamaged archives, the original color features of the archives are accurately reproduced, the interference of human subjective factors on the restoration result is effectively reduced, and the authenticity and color consistency of the restored image are improved; due to the introduction of the local adaptive enhancement technology, the contrast ratio of each local area of the archive image is more prominent, and the problem of overexposure or detail missing possibly caused by overall unified enhancement is avoided.

The invention provides a metal product surface paint spraying color quality inspection method based on deep learning, and the method comprises the following steps: 1, determining a to-be-detected metal workpiece paint spraying color and a standard color card color, collecting a data set, carrying out the processing and data labeling of the data set, and carrying out the format conversion of a generated labeling file through a script; 2, a color recognition model is constructed, namely, YOLOv5 and Resnet-18 are used for color recognition, and color type recognition of the metal workpiece after image input is achieved; step 3, constructing a color consistency evaluation model, that is, performing target segmentation by using a U-net network, and then performing twin neural network training to perform qualification inspection; 4, adjusting the parameters of the improved model to obtain a network model, and comparing the performances of the two models; and finally, shooting a workpiece picture under an actual condition to verify the applicability of the model, and analyzing the advantages of the model in different scenes. By applying the technical scheme, the pixel-level segmentation prediction of the workpiece can be realized.

The invention relates to an intelligent optimization method and system for LED light source module chip mounting in the new generation information technology field, and the method comprises the steps: collecting the initial spectrum data and position coordinates of each chip through a wafer scanning device, processing the data through a spectral analysis method, and obtaining a preliminary photoelectric parameter distribution diagram of each chip; collecting overall photoelectric response data from the mounted module substrate, analyzing color distribution by adopting an image processing technology, and if an analysis result shows a non-uniform area, marking the area to obtain a list of chips needing to be adjusted; according to the chip list needing to be adjusted, the genetic algorithm is input again for local optimization, the algorithm considers the characteristics of adjacent chips, whether the uniformity threshold value is met after optimization is judged, and a final adjustment scheme is determined; and guiding the laser lift-off equipment to remove and re-mount the specified chip through the final adjustment scheme, and collecting verification data after re-mounting to obtain the display module meeting the color consistency standard.

The invention discloses a multi-view camera splicing monitoring system based on a sky target, and relates to the technical field of visual intelligence. The multi-view camera splicing monitoring system based on the sky target comprises the following steps: a multi-view data acquisition module used for acquiring multiple types of data sets and carrying out time alignment and dimensionless normalization; the target detection and luminosity reference construction module is used for carrying out sky target detection and standard color block analysis and constructing a multi-view luminosity reference library; the deviation estimation and mapping construction module is used for constructing a cross-camera global luminosity mapping model; the adaptive control and real-time correction module is used for regional luminosity correction and illumination change detection; and the panoramic stitching and evaluation feedback module is used for constructing a joint area luminosity consistency evaluation index. According to the method, the multi-view splicing brightness color consistency and the target detection robustness under complex illumination are effectively improved, and the problems of seam brightness gradient and difficult color cast elimination caused by independent automatic exposure and white balance are solved.

The invention discloses a color consistency calibration system for AMOLED flexible display, which relates to the field of display and comprises a deformation sensing module, a color acquisition module, an aging state sensing and dynamic compensation module, a user personalized color gamut adaptation module, a data fusion processing module, a self-adaptive calibration algorithm module, a calibration execution module and a real-time feedback verification module. According to the AMOLED flexible display color consistency calibration system, screen body stress and deformation data are captured through deformation perception, the pixel attenuation state is monitored through aging perception, a personalized module adapts to user color preference, partition integrated calibration parameters are generated through data fusion and a self-adaptive algorithm, a closed loop is formed through calibration execution and real-time feedback, and the color consistency of the AMOLED flexible display is calibrated. According to the AMOLED flexible screen color consistency calibration method, the defects that deformation-aging composite drift is ignored and personalized adaptation is lacked in the prior art are overcome, full-scene and full-life-cycle color consistency calibration of the AMOLED flexible screen is achieved, accuracy and user experience are considered, and the AMOLED flexible screen color consistency calibration method is suitable for various flexible display terminals such as folding screens and curled screens.

The invention provides a color characterization method for an optical perspective augmented reality system, and the method comprises the steps: carrying out the full-view-field measurement of different environment illumination conditions and OST AR virtual stimulation colors, building a mathematic relation between a virtual display color which should be outputted by AR display, a human eye perception color, and ambient light, and enabling the mathematic relation to describe the color characterization of the virtual display color under different illumination conditions. According to AR display, how to adjust own color output is used for compensating the influence of ambient light on final perception colors, so that the accuracy and consistency of color reproduction are improved; that is to say, the nonlinear influence of the ambient light on the AR virtual display content is corrected through the nonlinear mixed compensation model of the ambient light measurement and the OST AR virtual stimulation color, and the human eye perception color deviation of the OST AR equipment in the complex illumination environment is corrected. The color consistency of OST AR display in an illumination environment and the color rendition capability of an OST AR system under different illumination conditions are improved, and a more natural virtual-real fusion effect is achieved.

The invention relates to an intelligent color box printing color analysis control method, and provides a reinforcement learning decision closed-loop control method integrating distributed color detection, space-time multi-modal data processing, feature extraction and self-supervised clustering and multi-scale reward modeling. Multi-variable normalization and synchronization are completed through multi-point real-time collection of color, process and environment data, color change meta-state features are extracted based on a self-encoding neural network, and a meta-state distribution model related to space and working conditions is established through self-supervised clustering. And the color risk is evaluated in combination with a multi-scale reward function, so that color parameter optimal adaptive adjustment and cooperative control driven by reinforcement learning are realized. In an actual control link, the system can dynamically optimize a meta-state aggregation and strategy network structure based on a feedback signal, a stable and self-adaptive color consistency closed-loop control system is constructed, and the color consistency and process intelligence level of a printed product under a complex working condition are effectively improved.