187 results about "Color consistency" patented technology

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.

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 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 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 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 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 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.

The application provides a deep-sea underwater sequence image enhancement method and device, electronic equipment and storage medium. Strong prior information is extracted from an obtained original deep-sea underwater sequence image and target object segmentation is performed, and a degraded region is detected based on a segmented target object region. A global background light is estimated from an edge region of the degraded region through a physical model, the estimation accuracy of transmittance is improved by using a color line prior theory, the degraded region is repaired according to the optimized transmittance, and a restored deep-sea underwater sequence image is obtained. The restored deep-sea underwater sequence image is enhanced by combining a FUnIE-GAN network and a suirSIR network. Color transfer is performed according to underwater sequence image content similarity, and sequence image color consistency is ensured. The application realizes color, illumination and definition enhancement of the deep-sea underwater sequence image, maintains sequence image color consistency, and ensures time smoothness of the output sequence.

This invention discloses a method, system, device, and storage medium for generating camouflage images, belonging to the field of computer vision and image generation. It employs a hierarchical automatic annotation method based on a large visual language model to obtain camouflage image annotation data, guiding the process through a three-level annotation workflow: online batch processing, offline supplementation, and manual review, outputting structured results. Based on a diffusion model framework, it integrates two types of feature enhancement modules—texture prior and color adaptation—to construct a generative model. After training with annotation data, it inputs the camouflage image to be restored, constraining texture consistency and aligning global color consistency through the two modules respectively, thus completing the generation and restoration of the camouflage image. This method for generating and restoring camouflage targets, combining semantic pairing, texture prior, and color consistency, along with its accompanying dataset, can be widely applied to camouflage target image synthesis, camouflage target detection enhancement, augmented reality, and other technical directions.

The invention relates to image optimization, in particular to an image quality optimization method based on multi-branch collaboration, which comprises the following steps of: inputting a denoising feature into a local information enhancement branch, and enhancing local texture information of a dark region to obtain a local enhancement feature while keeping the stability of an original structure of a low-light image; inputting the de-noising features into a global information enhancement branch, and performing global color information modeling and compensation on the de-noising features for the difference of the low-light image in the aspects of overall brightness distribution and color consistency to obtain color features; denoising features are input into a texture enhancement branch, high-frequency structure information is intensified in a targeted mode through a feature enhancement mechanism combining explicit texture guidance and implicit attention modeling, so that lost detail edge and local texture information of a low-light image after denoising is restored, the definition and visual detail expressive force of a finally reconstructed image are improved, and the image quality is improved. Obtaining texture features; the method can overcome the defect that brightness enhancement, texture recovery and color correction cannot be realized at the same time.

The application relates to the technical field of lenses and provides a lens effect evaluation method and device based on image vision, equipment and a medium. Images respectively photographed by multiple lenses in a camera device are acquired; the images are spliced to generate a panoramic image; exposure parameters are generated by detecting the synchronization degree of the exposure states between the lenses based on image parameters corresponding to the images and the panoramic image; panoramic parameters are generated by detecting whether the coverage range corresponding to the panoramic image is complete; color parameters between the lenses are generated based on the color distribution in the images; and a lens effect evaluation report is generated according to the exposure parameters, the panoramic parameters and the color parameters. The exposure synchronization degree, the panoramic integrity and the color consistency of the lenses can be accurately and comprehensively evaluated in the application scene of multiple lenses, comprehensive quantitative indexes are provided for the multiple-lens effect evaluation, the comprehensiveness and accuracy of the generated image information under the multiple lenses are ensured, and the visual effect and the user experience of the product are improved.

The invention discloses a scene light color consistency implementation method, device and equipment and a storage medium, and the method comprises the steps: obtaining lamp types to be applied to the same illumination scene, the lamp types at least comprising two of a lamp strip, a neon, a linear lamp and a barrel spotlight; acquiring the working current of each light-emitting chip corresponding to the lamp strip, the neon, the linear lamp and the cylindrical spotlight under each standard color temperature test; calculating the working current proportion of each light-emitting chip under each standard color temperature according to the working current of each light-emitting chip of the lamp strip, the neon, the linear lamp and the tube spotlight; and when a user selects the corresponding standard color temperature, obtaining the working current proportion of each light-emitting chip of the lamp strip, the neon, the linear lamp and the tube spotlight according to the standard color temperature, and controlling the corresponding light-emitting chips to operate according to the working current proportion. According to the method, the high consistency of the light color in the lighting environment is realized, and the user experience is greatly enhanced.

This invention provides a remote sensing image information reconstruction method based on isoluminance constraints and color structure control. The method inputs a registered target image, a reference image, and a mask file marking missing regions. Utilizing spatial information outside the missing regions in the target and reference images, a color structure consistency constraint is constructed to solve for partially missing pixels in the target image's missing regions. The target image and mask file are updated based on the solved partial pixels. Combining the information corresponding to the missing regions in the updated reference image, the isoluminance and gradient weights of the pixels to be identified within that region are calculated. An isoluminance equation is constructed based on the calculated isoluminance and weights, and the reconstructed image is obtained by solving the equation, thus achieving the repair of missing regions in the remote sensing image. This invention is the first to propose calculating isoluminance based on weighted gradient values ​​of each pixel in each direction, forming an image isoluminance network, constructing an isoluminance equation to solve for pixels in missing regions, and improving reconstruction accuracy through color consistency constraints.

The invention relates to the technical field of stoma skin analysis, and discloses a stoma surrounding skin analysis method and system based on image processing, and the method comprises the steps: firstly obtaining an original image containing a stoma, surrounding skin and a part of a stoma bag chassis, and carrying out the preprocessing; then, accurately separating skin and chassis areas through multi-target semantic segmentation; then, the chassis is used as an opportunity reference object, and color calibration is carried out on the image by comparing the actual observation color with the standard color, so that the difference between illumination and equipment is eliminated; and finally, quantized color and texture features are extracted from the calibrated skin area to form a feature vector, and skin state analysis is performed according to the feature vector to generate a report. Therefore, an additional color card is not needed, color calibration is carried out by utilizing inherent elements in the image, the problem of color consistency of image analysis in a home environment is effectively solved, and automatic, objective and standardized evaluation of the state of the skin around the stoma is realized.

A condenser (100) and a lighting system (1000). The lighting system (1000) comprises a condenser (100) and a light source (200) connected to the condenser (100), wherein the condenser (100) comprises a condenser body (10), and a light mixing lens (20) and a condensing lens (30) which are arranged on the condenser body (10). The condenser (100) firstly mixes, by means of the light mixing lens (20), light emitted by the light source (200), and then condenses the light by means of the condensing lens (30), such that the projected light is uniform, thereby effectively reducing disordered light spots, making the light integrity better, and having a better lighting effect. The condenser (100) is particularly suitable for the light source (200) having multi-color or double-color lamp beads, can effectively improve the color consistency of light emitted by the light source (200), enhances the light efficiency, and efficiently meets diverse use requirements of users.

The utility model discloses an inclined plane bowl cup device capable of improving reliability and viewing angle consistency, which relates to the technical field of LED bowl cups and comprises a base and a side wall, three primary color lamp beads are arranged on the base, the side wall is arranged along the side line of the base, the side wall is arranged around the base, and the side wall is arranged on the side line of the base. The side wall located on the upper side is longer than the side wall located on the lower side, the inclined face faces audiences, the normal direction of light rays just coincides with the sight line, the light intensity and color consistency in the direction are the best, the normal direction of the device faces downwards, and light interference and specular reflection caused by the fact that peripheral light rays directly irradiate the device are avoided.

The application provides a compensation light mixing method of a color temperature adjustable lamp, and relates to the technical field of illumination control, and comprises the following steps: in a factory detection stage, a first LED lamp string and a second LED lamp string are caused to emit light together at a middle color temperature grade, a compensation LED lamp string is not connected, and a basic light mixing color point is detected; the compensation LED lamp string is connected in at least two candidate compensation states, and a compensation light mixing color point is detected; a target compensation grade is determined according to the basic light mixing color point, the compensation light mixing color point and a black body locus reference point; the target compensation grade is solidified as a compensation connection parameter; and in a use stage, a switching unit controls the compensation LED lamp string to be connected to a current loop according to the compensation connection parameter. According to the method, the compensation connection parameter can be determined according to the factory detection result, and the light color consistency at multiple color temperature grades is improved.

The utility model relates to the field of lighting, and discloses an LED light source device and lighting equipment. The white ink layer is positioned on the upper surface of the substrate; mounting holes are distributed in the white ink layer; the LED chip is positioned on the upper surface of the substrate and is electrically connected with the substrate through the mounting hole; the white glue reflecting layer is positioned on the surface of the white ink layer and is positioned around the LED chip; and the fluorescent colloid is positioned on the surface of the white glue reflecting layer and covers the LED chip. The white glue reflecting layer is located on the upper surface of the white ink layer, and the white glue reflecting layer has the characteristic of high temperature resistance, is not prone to discoloration at the high temperature and ensures long-term lighting effect stability, so that a good reflecting effect can be achieved on light emitted by the LED chip, the lighting effect of the LED light source device is improved, and the service life of the LED light source device is prolonged. And the problems of color temperature dispersion and poor color consistency can be solved, and the chromaticity stability is improved.

The present disclosure provides a method for image color correction, a training method and device of a neural network model. The method comprises: obtaining an original image and an enhanced image corresponding to the original image, the enhanced image being obtained by image enhancement on the original image; and generating a color offset tensor based on the original image and the enhanced image, the color offset tensor being capable of being used for correcting the color of the enhanced image; generating a multi-scale color consistency loss function based on the original image, the enhanced image and the color offset tensor; and updating the neural network model based on the multi-scale color consistency loss function to obtain a trained neural network model. Thus, by training an end-to-end neural network for correcting color distortion of an image generated in an image enhancement process, the color distortion problem of an output image of a current deep learning image enhancement model is overcome.

The present disclosure relates to the technical field of light emitting diode (LED) packaging, and in particular to an LED package and an LED lamp bead. The LED package includes a reflector cup bracket hollow to form a housing space. A first adhesive layer, a second adhesive layer, and a color conversion layer arranged between the first adhesive layer and the second adhesive layer are arranged in the housing space. When the LED package is packaged with an LED chip, the color conversion layer can be directly used for cutting, and the LED package can be directly attached above the LED chip for color conversion, which not only ensures the color consistency of a device, but also avoids the failure of a film material contacting with water vapor, thereby saving the cost of the entire film material.

The invention provides a self-adaptive image enhancement method and device in a low-illumination scene. The method comprises the following steps: performing low-illumination scene judgment on an RGB image to obtain a scene category, and performing self-adaptive partition processing on the RGB image according to the scene category to obtain a plurality of area images; extracting illumination features, noise features and detail features of each regional image, and generating a feature matrix according to the features; dynamically matching an image enhancement algorithm or an algorithm combination for each area image from an enhancement strategy library through a preset matching strategy according to the scene category and the feature matrix; performing image enhancement processing on the corresponding area image by adopting an image enhancement algorithm or algorithm combination matched with each area image; and sequentially carrying out cross-partition fusion, color consistency calibration and naturalness optimization processing on each region image after enhancement processing, and outputting an RGB enhanced image. According to the method and the device, the brightness, the detail definition and the color consistency of the RGB image in the low-light environment are effectively improved.

The invention discloses a multicolor LED built-in IC multicolor color linkage control system and a multicolor LED built-in IC multicolor color linkage control method, relates to the technical field of embedded control and communication driving of addressable multicolor LED pixel unit lamp strips or lamps, and is used for solving the problems that linkage multicolor is easy to jump and flicker at a hue boundary and the color consistency between pixel units is difficult to maintain for a long time. Resetting and enumerating the serially connected pixel units by the master control, loading channel gain, gamma and current calibration parameters, and establishing linkage object mapping and coupling weight; generating a target color on an HSV track according to a control period, and suppressing kick at a hue boundary through vector synthesis and weight gating; and then calibration quantization, frame sealing and protocol coding sending are completed, regional fantasy colors are achieved, and closed-loop calibration is carried out in combination with current or optical feedback.