107 results about "Color correction" patented technology

The patent application provides a complex environment three-dimensional scene reconstruction method based on a multi-view neural network, and the method mainly comprises two steps: scene information capturing: obtaining scene data from a plurality of views through a high-resolution camera, and ensuring that all details of a complex scene are covered; the captured image is standardized, including color correction and noise removal, to ensure image quality and consistency. And three-dimensional model reconstruction: performing three-dimensional reconstruction on the acquired scene data by using a neural network. Firstly, light sampling is performed on a scene through camera parameters and pose information, and an occupancy grid is created to optimize the sampling efficiency. Thirdly, decomposing the sampling data into feature vectors and inputting the feature vectors into a neural network which is divided into a density network and a color network and is used for generating volume density and color information of the scene; and finally, obtaining a three-dimensional scene model through a volume rendering technology. The method has the advantage that a high-precision three-dimensional model can be quickly generated in a complex environment through an efficient sampling and data processing technology.

This invention relates to the field of projection equipment technology, specifically providing a temperature-adaptive color correction system and method for DLP projection equipment. The system includes an optical temperature monitoring module comprising four temperature sensors for temperature monitoring; a main control processing module using a microcontroller as the core processor, comprising four ADC channels for color compensation calculation; and an execution drive module comprising a first subunit (RGB laser driver circuit connected to the microcontroller), a second subunit (multi-zone fan control circuit), and a third subunit (power management circuit) for heat dissipation control. The microcontroller is connected to an EEPROM memory chip via an I²C bus. A heat dissipation structure module includes four axial fans and corresponding air ducts for each fan. Multiple temperature sensors are placed on key optical components such as the RGB laser and DMD chip to monitor the actual operating temperature of each area in real time.

The present application belongs to the technical field of oil use peony phenotype parameter automatic extraction method, especially relates to a kind of oil use peony phenotype parameter automatic extraction method and system based on multi-organ image analysis, obtain the high-resolution original image of oil use peony multi-organ containing ColorChecker24 standard color card, the organ at least includes petal, blade, fruit pod;Color correction and geometric correction are executed to original image, 24 color blocks of color card are positioned using computer vision algorithm, color conversion matrix is constructed to map image from device-dependent RGB space to standard color space, while calculating pixel scale based on color block physical size;Four corner points of bearing tray are detected for fruit pod image, and perspective transformation matrix is constructed to complete perspective distortion correction.

This invention proposes a method and system for intelligent ink volume prediction and adaptive color correction in printing equipment. The method includes: acquiring image data of the page to be printed, performing region segmentation and neighborhood connectivity analysis to generate a structure-weighted effective ink density, and calculating a page-level ink volume prediction value in conjunction with a paper correction factor; mapping the ink volume prediction value and effective ink density to the printhead's basic jetting parameters and the scanning controller's structural constraint parameters; performing linear gain and nonlinear convergence processing on the original color channel values ​​based on the above parameters to generate corrected channel values; driving the printhead to print, and sampling the printed scanned tape using a linear optical acquisition unit; and performing residual compensation and convergence constraint correction on the execution values ​​of subsequent scanned tapes based on the difference between the measured values ​​and the target values. This invention can improve the consistency and accuracy of printed colors.

A color correction method for filter-less pixels is disclosed. In one embodiment, an image sensor, having a pixel array, includes a plurality of filter-less pixels. Each pixel of the plurality of filter-less pixels includes a blue light region, a green light region, and a red light region. Incoming light successively passes through the blue, green and red light regions, and a readout circuitry, configured to receive color signal outputs comprising blue region signals from the blue light region, green region signals from the green light region, .red region signals the red light region, and correct color signal outputs with a correction algorithm, including a blue light correction algorithm, a red light correction algorithm, and a green light correction algorithm, The method is configured to output corrected color signals provided by the correction algorithm.

The present application relates to the technical field of projectors, and specifically provides a kind of intelligent projector control method based on color correction and depth correction, image and depth information of projection plane are collected by optical perception device, and color deviation tensor and depth deviation tensor between it and ideal plane are calculated, color correction and depth correction are carried out on pre-projection image according to color deviation tensor and depth deviation tensor, so that projected image will not be affected by color and flatness on projection plane.The present application avoids color interference and flatness influence of projection plane, ensures that projector can realize flat, achromatic projection under visual effect in extreme conditions such as plane and space irregularity, color stain and the like, and solves the problems such as precision limitation, single scene and high cost in existing projection mapping application.

The present disclosure relates to the technical field of sensor control, and particularly provides a sensor module fault processing method and a printing device. The method comprises: sending a first self-checking instruction to a color correction sensor in a sensor module in a first state, wherein in the first state, a cover covers the color correction sensor; if it is determined that the color correction sensor is normal based on first self-checking information, sending a first moving instruction to the cover, wherein the first moving instruction is used to instruct the cover to move in a first direction, so that the sensor module enters a second state, and in the second state, the cover uncovers the color correction sensor; and determining a first fault detection result of the sensor module based on the intensity of a first feedback signal collected by the color correction sensor, wherein the first feedback signal is a signal collected by the color correction sensor in the process of the cover moving in the first direction. The present disclosure improves the accuracy and reliability of fault diagnosis of the sensor module.

The present specification provides a training method of an artificial neural network model for correcting image color and an image correction method using a trained artificial neural network model. The training method of the artificial neural network model according to the present specification uses, as training data, a pair of images before and after color correction. In the training method, the image before color correction is input into the artificial neural network model, N parameter values (N is a natural number) are obtained from the artificial neural network model, and at least one color correction program is executed according to the obtained parameter values, whereby a converted image, the color of which has been converted from the image before color correction contained in the training data, may be obtained. In addition, the artificial neural network model may be trained such that a difference value between the image after color correction contained in the training data and the converted image is reduced.

This invention relates to the field of high dynamic range imaging technology and discloses a deep learning-based high dynamic range imaging method. The method includes acquiring original images with different exposure types; extracting features from the original images; using a cross-frame entanglement mechanism to perform quantum motion compensation on abnormal exposure feature maps based on normal exposure feature maps, resulting in compensated abnormal exposure feature maps; fusing features between the normal exposure feature maps and the compensated abnormal exposure feature maps using a multi-scale feature pyramid and adaptive spatial attention, resulting in fused features; and reconstructing the image using an attention-guided residual reconstruction model to obtain a high dynamic range image. This invention solves the motion offset problem between multi-exposure images through quantum motion compensation, eliminating dynamic blur; achieves detail enhancement and color correction through a multi-scale feature pyramid and spatial attention; and reduces computational complexity and improves reconstruction efficiency through a lightweight reconstruction model.

This invention discloses a two-stage low-light image enhancement method and apparatus based on the HVI color space. The method includes: converting a low-light RGB image to the HVI color space to obtain a luminance feature map and a color feature map; performing global denoising on the luminance feature map and global denoising and global color correction on the color feature map through feature extraction and bidirectional interaction between features; performing local detail enhancement on the initially enhanced luminance feature map and local color correction on the initially enhanced color feature map through bidirectional interaction between features extraction and local features, obtaining fully enhanced luminance and color feature maps; and reconstructing the fully enhanced luminance and color feature maps into a low-light enhanced RGB image. This method achieves a dynamic balance between noise suppression and detail preservation, enhancing image texture, edges, and other minute structures while accurately repairing local color shifts and color overflow issues.

The application discloses a three-dimensional look-up table (3D-LUT) construction method and a color correction method based on FPGA, wherein the construction method comprises: interval sampling a 3D-LUT from three dimensions to form a three-dimensional look-up table, and the three-dimensional look-up table comprises: eight Sub-LUTs for storing split 3D-LUT data; wherein N is the sampling point number of three RGB channels in the 3D-LUT; the 3D-LUT contains (N-1) 3 cubic units; the Sub-LUT contains (M-1) 3 cubic units; and M is the sampling point number of three RGB channels in the Sub-LUT. The method can reduce BRAM resource occupation and is suitable for the technical field of image and video processing.

This application provides a color correction method, apparatus, device, medium, and product for a screen, the screen including a 2D screen and a lens layer; the method includes: acquiring a test image of the screen and the grayscale values ​​of sub-pixels in the test image; searching for color correction parameters at different observation positions to obtain color correction parameters at the current observation position; wherein, the color correction parameters at the observation position characterize the difference between the grayscale value of each sub-pixel and the actual pixel value at that observation position; and correcting the grayscale values ​​of sub-pixels in the test image according to the color correction parameters at the current observation position of at least one user. The solution of this application is used to correct color cast in 3D displays.

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 application discloses a tunnel water mist environment under inspection image de-scattering enhancement and reconstruction method, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a polarized image sequence and pose information collected synchronously; decoupling a scene by using a fusion neural network to output a fog-free image, a geometric gradient map and a water mist concentration map; calculating a frequency domain fusion weight map by using a frequency domain attention generation network; performing multi-scale frequency domain decomposition on the fog-free image to obtain high-frequency components and low-frequency components; performing pixel-level weighted fusion and inverse transformation according to the weight map to generate a preliminary enhanced image and perform color correction. The application adopts a physical constraint deep learning and a frequency domain adaptive fusion mechanism, can effectively eliminate the interference of tunnel water mist scattering, and improves the definition, detail performance and color authenticity of the inspection image, thereby providing high-quality visual support for tunnel safety monitoring.

This invention discloses a machine learning-based method and system for identifying exhaust gas particles, relating to the field of image processing technology. By acquiring exhaust gas particle images, feature extraction is performed to obtain color cast features. Based on these color cast features, the exhaust gas particle images are color-corrected to obtain a color-corrected image. Particle distribution features are extracted from the color-corrected image using a target model. These particle distribution features are then fused with the color-corrected image to obtain an adaptive transmission map. The backscattering coefficient is calculated based on the adaptive transmission map, and the adaptive transmission map is enhanced based on the backscattering coefficient to obtain a final enhanced image. Exhaust gas particles are identified based on the final enhanced image. Through color correction, adaptive transmission map construction, and backscattering enhancement, the color accuracy and particle distribution visualization of exhaust gas particle images are improved, along with image clarity and detail, thereby enhancing the accuracy and reliability of particle identification.

This invention belongs to the field of vision and image processing technology, specifically providing an underwater image enhancement method and system based on latent space diffusion and adaptive style transfer. First, underwater and non-underwater image datasets are constructed and preprocessed using standardization. A pre-trained CNN is used to extract features from non-underwater images at multiple levels, calculating the Gram matrix of the convolutional layers as style features. The underwater images are input into a variational encoder, mapped to a low-dimensional latent space to obtain feature vectors. A diffusion denoising network predicts and removes noise in the latent space. An adaptive style transfer network combines the style features of the non-underwater images to achieve decoupling and fusion of content and style. Finally, the enhanced underwater image is reconstructed by a variational decoder. This invention solves the problems of existing methods relying on paired data, high computational complexity, and lacking an effective decoupling mechanism, achieving efficient underwater image color correction and sharpness enhancement.

A method is disclosed comprising modifying a color correction function intended to correct the initial chroma components of a current image to obtain normalized corrected chroma components to avoid clipping of the initial chroma components while maintaining saturation and preserving hue.

The application discloses a large-scale remote sensing image color correction method based on block local histogram matching, belongs to the technical field of remote sensing image processing, and aims at the problems of low calculation efficiency, neglect of local features and difficulty in maintaining overall color tone when existing methods process large-scale orthographic images. The correction is realized through five steps of adaptive image blocking, multi-scale statistical feature extraction, hierarchical nonlinear mapping model, high-precision seamless splicing and parallel computing framework optimization. The method can efficiently process massive data, takes into account local correction accuracy and overall color tone consistency, improves robustness through intelligent parameter adaptive adjustment, significantly eliminates image color differences, is suitable for multiple fields such as land resource investigation and city planning, and provides a high-quality solution for large-scale image splicing.

This invention relates to the field of medical image processing technology, and proposes an intelligent method for color calibration of dental photographs and an automatic color selection system for prostheses. The method includes: receiving an oral image containing a shade guide and teeth; identifying the shade guide and target teeth in the oral image; obtaining the distorted color value of the shade guide; constructing a color difference conversion model based on the distorted color value and pre-stored standard color values ​​of the shade guide; and performing color correction on the target teeth in the oral image based on the color difference conversion model to obtain a corrected image. This invention solves the problem of tooth color distortion in oral images.

The present disclosure provides an image color correction method and apparatus, a color correction robot, and a storage medium. The method comprises: acquiring an image to be processed; extracting color values of pixels in a plurality of color blocks in the image to be processed to obtain a color value list; setting a weight coefficient for each color value in the color value list; using the color value list having the weight coefficients to fit a polynomial regression model to obtain a fitted polynomial regression model; using the fitted polynomial regression model to generate a color conversion matrix, wherein the color conversion matrix is used for representing a mapping relationship between the color values in the image to be processed and standard color values in a color chart; and using the color conversion matrix to correct the color values of the pixels in the image to be processed, to obtain a corrected image. The present embodiment improves the color accuracy of an image and ensures the reliability and authenticity of image information, thereby providing reliable data support for subsequent intelligent analysis and assisted diagnosis.

Embodiments of the present application provide a kind of MR equipment color correction method and related device, it is related to mixed reality technical field, the method comprises: obtaining target image to be displayed;Obtain the frequency domain data related to light source environment of the target image;In the case where it is determined that discrete spectral light source environment and continuous spectral light source environment exist in the target image according to the frequency domain data, the target image is regionally segmented, and discrete spectral light source region, continuous spectral light source region and transition region are obtained;The image corresponding to each region is respectively sampled to carry out color correction on the color correction model of preset;Display the target image after color correction.The target image is segmented, and different color correction parameters are used for correction for the different light source environment images segmented, effectively solve the color consistency problem of MR equipment under complex light source environment, provide more immersive and natural mixed reality experience for user.

This invention discloses a method for detecting the freshness of giant salamander meat based on color constancy and integrated intelligent vision, belonging to the fields of food safety and computer vision technology. The method includes: acquiring RAW images of the meat sample and an indicator film and resolving them into linear radiosity tensors; constructing a color correction model using a latent diffusion local redraw network and a conditional control network; synthesizing a structure-constrained virtual reference color card in a masked region; extracting ambient light source features and performing adaptive white balance compensation to output a standard color image; inputting this image into a deep network optimized by ArcFace to extract a high-dimensional feature vector; and finally inputting this vector into an XGBoost model to fit a nonlinear decision boundary and outputting a freshness rating. This invention effectively reduces color shift interference from complex light sources, improves the recognition accuracy of samples in the critical period of color change, and provides a highly robust solution for non-destructive monitoring of giant salamander meat freshness in actual market environments.

The application provides an image color correction method, comprising: acquiring a target image containing a plurality of color blocks collected by a target device, and extracting CIE Lab color data of each color block of the target image; acquiring a source image containing a plurality of color blocks collected by a device to be debugged, and converting to a CIE Lab color space; performing segmented correction on a luminance component of the source image in the CIE Lab color space to obtain corrected CIE Lab color data; determining a plurality of corrected CIE Lab color data corresponding to a plurality of candidate color correction matrices based on the corrected CIE Lab color data; calculating a plurality of color differences between the source image and the target image based on the CIE Lab color data of the target image and the plurality of corrected CIE Lab color data of the source image; and determining that a candidate color correction matrix corresponding to a minimum color difference is a target color correction matrix; wherein the target image and the source image correspond to the same photographed object. The application can realize fast and accurate matching of an image color effect of the device to be debugged to an expected color tone or a color effect of the target device.

This invention discloses a reinforcement learning-based underwater image enhancement training optimization method, belonging to the field of underwater image enhancement technology. Addressing the problems existing in current technologies, this method treats low-frequency color correction and high-frequency texture reconstruction in underwater image enhancement as two enhancement objectives with different optimization focuses. It utilizes a reinforcement learning mechanism to dynamically adjust the training strategies for both, solving the problem that existing fixed training strategies are difficult to adapt to changes in training states. A frequency-domain perceptual state representation is constructed by combining low-pass filtering and residual decomposition to explicitly describe the relative optimization state between low-frequency color restoration and high-frequency texture restoration during the current training process, providing a decision-making basis for the policy controller. An adaptive training optimization mechanism based on frequency-domain state feedback is built, automatically completing policy scheduling according to the actual restoration state during training, reducing the reliance on manually designed training strategies and improving the adaptability, generalization, and scalability of the method in complex underwater degradation scenarios.

Apparatuses, systems, and methods related to an image processor formed in an array of memory cells are described. An image processor as described herein is configured to reduce complexity and power consumption and / or increase data access bandwidth by performing image processing in the array of memory cells relative to image processing by a host processor external to the memory array. For instance, one apparatus described herein includes sensor circuitry configured to provide an input vector, as a plurality of bits that corresponds to a plurality of color components for an image pixel, and an image processor formed in an array of memory cells. The image processor is coupled to the sensor circuitry to receive the plurality of bits of the input vector. The image processor is configured to perform a color correction operation in the array by performing matrix multiplication on the input vector and a parameter matrix to determine an output vector that is color corrected.

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