14results about How to "Avoid color distortion" patented technology

The invention discloses a color high contrast processing algorithm for four-color electronic paper, and relates to the technical field of electronic paper display technology and image processing, and the method comprises the specific steps: firstly, carrying out feature extraction, converting an original RGB image to an HSV space, carrying out the statistics of the pixel proportion of a brightness interval, screening red and yellow pixels, and calculating the feature and the proportion of four colors; a dynamic clustering center is generated; performing weighted clustering mapping to generate an intermediate image; enhancing the saturation and boundary contrast of the adaptive image in a linkage manner; and finally, modular adaptive output: selecting scheme optimization data, converting the scheme optimization data into a 2-bit format, and outputting the two-bit format after verification. According to the method, through multi-dimensional feature extraction and dynamic clustering center generation, accurate color mapping is realized, and the color rendition degree and comfort degree are improved; and through multi-factor linkage enhancement and modular adaptive output design, the display effect and hardware adaptability are considered, the detail expressive force is enhanced, the integration process is simplified, reliable data transmission is guaranteed, and the application adaptation and display quality is improved.

This invention discloses an underwater image enhancement method based on cascaded correction and multi-scale fusion, mainly including the following steps: (1) establishing an underwater image dataset to form a first dataset; (2) constructing an underwater image enhancement model based on cascaded correction and multi-scale fusion; (3) inputting the first dataset into the model for image enhancement; (4) evaluating the enhanced underwater image to meet the index requirements, thus obtaining the final underwater image enhancement model based on cascaded correction and multi-scale fusion. Compared with the prior art, the underwater image enhancement method disclosed in this invention significantly enhances the clarity and color fidelity of underwater images, effectively improving the visual quality of underwater images.

The invention discloses an automatic map slice publishing method and system based on multi-source data. The automatic map slice publishing method based on the multi-source data comprises the following steps that multi-source map data are processed and sent; receiving multi-source map data, performing data matching processing, and performing multi-source publishing after data matching processing; and sending the published multi-source map data to a corresponding data processing cluster, and carrying out multi-source data slicing processing. According to the method, the multi-source data processing flow is integrated, manual operation of multiple platforms is not needed, automatic slice publishing can be completed only by simply configuring parameters, and automation of map slice publishing is achieved.

The invention discloses a self-adaptive dark channel image defogging method and device based on an FPGA (Field Programmable Gate Array). The method comprises the following steps: firstly, quickly generating a preliminary region mask based on gray information, and preliminarily separating a bright smooth region which may not meet dark channel prior; then, when it is judged that the area exists, an adaptive threshold value is dynamically calculated based on the color features of the area, the preliminary mask is accurately corrected, and a final mask for accurately distinguishing the target area from the non-target area is obtained. By using the precise mask, interference data of a target area is effectively eliminated in subsequent core parameter estimation, so that the problems of color distortion and contrast reduction caused by misalignment of parameter estimation in areas such as the sky and the like in related technologies are fundamentally avoided. Meanwhile, the whole mask generation and correction process is constructed based on efficient calculation operations such as gradient, threshold comparison and color statistics, is very suitable for being implemented on parallel hardware such as an FPGA (Field Programmable Gate Array) in an assembly line manner, and ensures the overall high execution efficiency of the algorithm.

The application relates to the technical field of target detection, in particular to a high-altitude electric power operation violation identification method, which discloses the following steps: collecting high-definition images and low-quality images of an electric power operation site by using a drone, and making an electric power site high-definition data set; constructing an image enhancement network based on a local information enhancement module and a global information enhancement module; building a violation identification model based on an improved YOLOv8; selecting a suitable optimizer and a loss function; training and optimizing the constructed model. The optimal model after training and optimization is converted into an RKNN model in the front end, and is deployed to the front end of the drone. Real-time detection is realized by relying on the camera in the front end, and whether the operation personnel violate the rules is judged by the judgment unit according to the detection result. The application provides a high-altitude electric power operation violation identification method, which considers the construction of an operation data set, the enhancement of low-quality images, and simultaneously proposes a violation identification network model based on an improved YOLOv8, so that the accuracy of detection is maximally improved.

This application provides an image contrast enhancement method, system, electronic device, and storage medium. The method includes: separating luminance and chrominance components from the YUV data of an input image; training the luminance component for contrast enhancement using an enhancement network model to obtain an enhancement coefficient grid for the luminance component and compensation coefficients for the chrominance component; enhancing the luminance component using the enhancement coefficient grid and enhancing the chrominance component using the compensation coefficients; and obtaining an enhanced image based on the enhanced luminance and chrominance components. This application can significantly reduce computational complexity while maintaining contrast enhancement effects, and can also suppress noise and eliminate video flicker.

The invention belongs to the technical field of image processing, and relates to a self-priori-guided image restoration method and system, and the method comprises the following steps: S1, constructing a damaged image data set; step S2, a step of network construction; step S3, a step of network training and optimization; step S4, a network performance test step; according to the technical scheme of the invention, aiming at the problems of content repetition, color distortion and the like which are easy to occur when a traditional image restoration method is used for processing large-area defect of a continuous region or reference content is insufficient, a two-stage image restoration method consisting of a semantic prior estimation network and a semantic prior guide restoration network is adopted; and residual semantic information in the damaged image is fully mined and utilized, so that stronger robustness is realized while the visual naturalness and detail accuracy of a restoration result are improved.

The application relates to the field of image restoration, in particular to an intelligent image recognition and restoration processing system and method based on big data, which comprises an image verification module, a selection restoration module, a gray scale restoration module, a color separation enhancement module and a color mixing filter module; the image verification module is used for determining image information loss; the selection restoration module is used for calculating the peak signal-to-noise ratio of the image; the gray scale restoration module is used for outwardly extending the gray scale texture of the image; the color separation enhancement module is used for separating the image colors by using different color separation schemes; and the color mixing filter module is used for carrying out image denoising and image color mixing processing through a filter. The application can avoid the data block effect, blurring and color distortion caused by network fluctuation, reduce the image restoration time, adapt to the viewing demand of users, reduce the influence of data loss on the image, optimize the calculation efficiency of the restoration algorithm, improve the picture texture visual sense and improve the visual experience of terminal users.

The utility model belongs to integrated protection technical field, concretely relates to a kind of intelligent integrated protection device in overhaul storehouse, including warning device, the warning device left side rotation installation has protective housing, the protective housing front part is provided with switch, the protective housing front part is provided with button, the protective housing front part fixed mounting has display screen, the protective housing front part fixed mounting has camera, the protective housing front part is provided with protection device, protection device includes motor, threaded rod, sleeve, cleaning rod, push rod, slide rail, protective block and contact plate, motor fixed mounting is in protective housing front part, threaded rod fixed mounting is in the output end of motor. The utility model is through the setting of protection device and solves the problem that it is difficult to protect camera, so that protective block can effectively prevent dust from entering during stop working.

The invention relates to the technical field of image processing, and discloses an image color style migration method and device, computer equipment and a storage medium. The method comprises the following steps: respectively carrying out preprocessing and zero centralization on a source image and a reference image; calculating covariance matrixes of the two images and performing singular value decomposition to obtain an orthogonal matrix and a diagonal matrix; performing parameterization adjustment on the singular value according to the selected color space mode, and supporting independent control of brightness and color; constructing a whitening transformation matrix and a coloring transformation matrix based on the adjusted matrix; sequentially applying the two matrixes to source image data to complete color transformation and reconstruction; and finally, performing color space protection and post-processing, and outputting a result image. According to the method, color channel correlation is modeled through the covariance matrix, mathematically rigorous color distribution migration is realized based on singular value decomposition, flexible parameter adjustment and robust color protection are provided, and the naturalness and controllability of color migration are effectively improved.

The application discloses a method for image defogging based on a mixed large-scale convolution attention mechanism, and the implementation steps are as follows: a large-scale convolution module for extracting multi-scale information of a feature image is constructed, a parallel attention module for extracting shared global information and position-related local information of an original feature is constructed, a mixed large-scale convolution network is built, the mixed large-scale convolution network is trained by using a generated training set, an image to be defogged is input into the trained mixed large-scale convolution network, and a haze-free image is output. The application overcomes the defects of the prior art, such as incomplete defogging of non-uniform fog images, insufficient learning of scene characteristics, and loss of image details and color distortion caused by neglecting multi-scale information of the image. The mixed large-scale convolution network can still achieve a high defogging effect in complex scene defogging.

The invention provides an eye burn recovery degree evaluation method and system based on image recognition, and the method comprises the steps: carrying out the preprocessing of a to-be-evaluated eye image, so as to obtain a processed eye image; performing color correction processing on the processed eye image to obtain a color correction image; performing texture enhancement and brightness adjustment processing on the color correction image to obtain a first adjustment image and a second adjustment image, and fusing the first adjustment image and the second adjustment image to obtain a fused image; performing tensor iteration de-noising processing on the fused image to obtain a de-noised eye image; and inputting the de-noised eye image into a pre-trained burn recovery degree evaluation model for evaluation so as to output a target burn recovery degree evaluation result, the image brightness is effectively improved, image color distortion is avoided, the problem of edge blurring is solved, detail information of the image is fully highlighted, and the image quality is improved. And the accuracy of subsequent model evaluation is improved.

The invention provides a submarine cable image splicing method based on double-domain decoupling enhancement and multistage fault-tolerant matching. The method comprises the following steps: acquiring submarine cable image pairs of adjacent frames, extracting original color channel information of each frame of image, and performing channel enhancement processing on each frame of image to obtain a channel enhanced image; processing the channel enhanced image based on the dual-domain decoupling enhancement network to obtain a final enhanced image; performing feature point matching on the final enhanced image pair based on a multistage fault-tolerant matching network to obtain mutually matched effective feature point pairs; and calculating a homography transformation matrix of the final enhanced image pair based on the effective feature point pair, carrying out image stitching on the final enhanced image pair according to the homography transformation matrix, and carrying out color enhancement processing on the stitched image based on the original color channel information to obtain a final stitched image. According to the technical scheme, the color distortion is effectively inhibited while the image contrast and the detail definition are improved.

The application relates to a video image processing method and device, electronic equipment and a storage medium. The method comprises the following steps: performing background and target separation processing on multiple images in a target video respectively to obtain multiple target background images and multiple first target images, counting the total number of pixel points in the multiple first target images and the number of times that each first pixel point in the multiple first target images appears in the multiple first target images; based on the ratio of the total number of pixel points to the number of times that each first pixel point appears, second pixel points that do not satisfy a probability threshold range are selected from each first pixel point in the multiple first target images; the pixel information of the second pixel points in the first target image is replaced by the pixel information corresponding to the pixel coordinates of the second pixel points in a preset reference image to obtain a second target image; and the second target image and the corresponding target background image are merged to obtain multiple target images. The video quality can be effectively improved.