60 results about "Histogram matching" patented technology

The invention discloses a video image color correction method and system based on artificial intelligence, and relates to the technical field of color correction, and the method comprises the steps: collecting video data, carrying out the image size normalization and denoising processing, and obtaining the preprocessed video frame sequence data; and extracting reflectivity data and illumination data by using a Retinex-Net deep learning model, calculating an illumination adjustment coefficient, carrying out pixel correction, and carrying out color temperature correction on the pixel data of the video frame subjected to pixel correction according to the illumination adjustment coefficient. According to the method, the preliminary color mapping matrix is generated through the color histogram matching method, the color distribution of the image can be optimized, the image is enabled to better conform to the color features of the target reference frame, the color temperature adjustment is performed on the image according to the illumination adjustment coefficient and the illumination threshold, the cold and warm tones of the video are enabled to be accurately adjusted, and the image quality is improved. Through successively carrying out pixel correction, color correction and local area correction, a stable color adjustment frame is provided, so that the video styles are consistent.

To employ low resolution, noisy tone mapping operations for high resolution images, at least one raw image frame is converted to a first image at a higher resolution and a second image at a lower resolution. Tone mapping is applied to the second image to derive a third image at the lower resolution. Histogram matching and regularization are performed to determine a lookup table approximating histogram matching of the second image to the third image. A global gain map is derived based on luma for the first image and the lookup table. A local gain map is derived by up-sampling and denoising residual differences between the third image and the lookup table applied to the second image. Based on the global gain map and the local gain map, a total gain map is determined for tone mapping the first image to produce a fourth image at the first resolution.

The invention provides an adaptive weight distribution chromosome stripe contrast enhancement method and system, and the method comprises the steps: firstly improving the significance of a chromosome stripe structure and an edge contour in a chromosome gray image through an edge enhancement convolution operator; carrying out chromosome monomer target detection and instance segmentation on the metaphase chromosome based on an instance segmentation network of deep learning to obtain a chromosome monomer ROI region; then, extracting a structure tensor response of the ROI region of the chromosome monomer, designing an adaptive enhancement weight of a histogram merging interval based on a normalized difference between a response value and global statistical information, and performing reconstruction and interpolation on a histogram of the image of the ROI region of the chromosome monomer; and finally, adaptively enhancing the contrast of the chromosome stripe through iterative histogram matching. According to the method, the original image texture structure is kept, and meanwhile, the definition and the distinguishability of chromosome stripes are effectively and adaptively improved.

A method for detecting defect in image is provided. The method obtains a number of original images, determines a first reference image from the original images, and performs a histogram matching on the original images excluding the first reference image according to the first reference image, to obtain a plurality of matched images. The method further generates a synthesized image according to pixel intensities of the matched images and pixel intensities of the first reference image; and uses the synthesized image as a second reference image to perform an image comparison with a test image, to generate a result of defect detection. A related device and a related non-transitory storage medium are also provided.

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 discloses an automatic color correction method for mass remote sensing images of a sub-meter satellite. Belongs to the technical field of automatic color correction of mass sub-meter satellite remote sensing images, and particularly relates to the technical field of an automatic color correction method for mass remote sensing images of sub-meter satellites. Mass sub-meter high-resolution pairing data are obtained by training an improved ResNet50 model, and then a histogram matching method is combined to obtain the color of the mass remote sensing images of the sub-meter satellites. And automatic color correction of remote sensing images of massive sub-meter satellites is realized. The method comprises the following steps: obtaining a sub-meter remote sensing image before correction, and carrying out histogram matching on the sub-meter remote sensing image before correction; pre-processing the sub-meter remote sensing image before correction and the corresponding RGB histogram; constructing and improving a ResNet50 model: adding a full connection module after a full connection layer of the ResNet50 model; and correcting a sub-meter remote sensing image through a ResNet50 improved model.

The invention discloses a discharge detection method and system for adaptive registration of ultraviolet and visible light images, and belongs to the technical field of image processing. An ultraviolet image and a visible light image of a discharge area of power equipment are respectively captured through an imaging module; motion vectors of a visible light image sequence are calculated according to optical flow motion estimation in a motion scene through a registration module and mapped to an ultraviolet image for displacement compensation, translation compensation is determined by adopting histogram matching in a static scene, and the motion vectors of the visible light image sequence are calculated according to the compensated image. Zooming the ultraviolet image according to the zoom scale calculated by the focal length and the central point of the ultraviolet image, and zooming the effective registration area of the cut ultraviolet image to a target size; and the electric detection module carries out electric discharge detection on the discharge point area according to the registered image. According to the method, the robustness is improved, boundary offset and nonlinear distortion are avoided, edge linear fitting is realized while accurate alignment of the center points is ensured, and the calculation efficiency is remarkably improved in cooperation with integer operation.

The invention relates to the technical field of underwater target detection, and discloses a reverberation suppression method and device based on tensor robust principal component analysis. The method comprises the following steps: arranging frame signals received by a sonar receiving array according to a time sequence to obtain a distance-azimuth echo sequence; performing histogram matching processing on the distance-azimuth echo sequence, aligning the intensity distribution of each frame to a reference frame, and obtaining a distance-azimuth echo sequence after matching processing; after the distance-azimuth echo sequence after matching processing is preprocessed, low-rank component estimation initialization processing is executed; and after the initialization processing is completed, executing an adaptive threshold iterative optimization process, and iteratively solving an iterative formula by using a scale gradient descent method to obtain a low-rank component and a target component. By applying the method, the problems of high target false alarm rate and insufficient detection precision caused by reverberation interference in a complex environment can be solved.

The invention belongs to the field of panoramic stitching digital image processing technologies and camera ISP technologies, and particularly relates to a chromatic aberration consistency control method based on front and rear end collaboration. The method comprises the following steps: 1, acquiring statistical information of multiple cameras; step 2, acquiring a current camera control parameter by adopting a front-end chromatic aberration consistency adjustment algorithm; 3, the current camera control parameters are issued to all the cameras, and all the cameras complete exposure imaging according to the current camera control parameters; step 4, acquiring an overlapped part image in each path of camera imaging; step 5, calculating a statistical histogram cumulative distribution function of overlapped part images in each path of camera imaging; step 6, calculating a histogram matching function according to the cumulative distribution function of the overlapped part images in each path of overlapped camera imaging, so that the histograms of the overlapped parts of the two images tend to be consistent; and step 7, according to the obtained histogram matching function, transforming the brightness channel of each pixel in the corresponding camera imaging to complete color difference consistency correction.

The application discloses a backdoor attack method based on color frequency injection and adaptive local enhancement, and relates to the technical field of machine learning and artificial intelligence security. The method comprises the following steps: introducing low-frequency color offset and weak high-frequency signal into an image in a CIELAB color space to perform global color-frequency injection; using a pre-trained proxy model to locate a high-sensitive perception domain of the model through mixed evaluation of gradients and class activation maps, and generating a binary mask; in an HSV color space, respectively applying nonlinear stretching factors to saturation and brightness of the sensitive domain based on the mask to perform adaptive local enhancement; using Gaussian smoothing, adaptive noise and histogram matching to eliminate edges and statistical abnormalities caused by local enhancement, completing compensation color enhancement to generate a poisoned image; and modeling a trigger core parameter as a constrained optimization problem, and using a particle swarm optimization algorithm to jointly dynamically update the trigger core parameter to obtain an optimal strategy. The application anchors the trigger feature depth in the core semantic area of the model and lurks in the normal data manifold, guarantees a high attack success rate, realizes extreme visual and feature concealment, and has strong anti-defense robustness.

The invention relates to the technical field of rock physics, discloses a histogram matching-based coal digital core modeling method, and is suitable for digital core reconstruction of experimental rock physics research. Comprising the following steps: S10, collecting a rock core and preparing a sample; s20, sample microstructure CT scanning and physical property parameter testing; s30, preprocessing the CT image; s40, performing rock component CT image segmentation based on a histogram matching algorithm; and S50, digital rock core reconstruction based on CT image stacking. On the basis of digital image processing, a rock physical experiment and a digital rock core technology are combined, and the histogram matching-based coal digital rock core modeling method is provided. The method is low in cost and high in operability, the reconstructed rock core and the original rock core have high matching degree, and a high-precision model can be accurately provided for physical property characteristic finite element simulation of the coal digital rock core.

The invention discloses a satellite remote sensing image fusion method based on Wrapping-Curvelet transformation, and the method comprises the steps: carrying out the spatial registration of a multispectral image and a panchromatic image, carrying out the resampling of the multispectral image according to bilinear interpolation, obtaining a multispectral image with the same pixel size as the panchromatic image, and carrying out the fusion of the multispectral image and the panchromatic image. The method comprises the following steps of: respectively performing histogram matching on a panchromatic image and three wave bands of a multispectral image to be fused to obtain a new panchromatic image, taking the three wave bands of the multispectral image and the corresponding panchromatic image as examples, respectively performing Wrapping-Curvelet transformation on the three wave bands of the multispectral image and the corresponding panchromatic image to obtain respective Curvelet coefficients, fusing the coefficients of each layer, and then performing inverse Wrapping-Curvelet transformation on the fused layers to obtain a new panchromatic image; and obtaining a fused image. The blocking effect caused by blocking is avoided, the number of parameters of the implementation method is small, redundancy is reduced, and the algorithm is simple, convenient and rapid.

The invention provides a general plane graph color migration method and system based on a depth diffusion model, and the method comprises the following steps: (1) carrying out the semantic segmentation of a black and white general plane line draft graph, and generating a semantic mask graph; (2) extracting global style features of the color general plane reference image and local color distribution features of different semantic regions; (3) inputting color features of the line draft image, the semantic mask image and the reference image into a depth diffusion model, restraining the structure of the generated image to be consistent with that of the line draft through a ControlNet module, and migrating the color features of the reference image to a corresponding position of the line draft according to a semantic region through a region perception attention mechanism; and (4) in the de-noising process of the diffusion model, combining the style consistency loss function and the regional color histogram matching loss to generate an image after color migration. According to the method, the color of the reference picture is migrated to the black-and-white line draft according to the semantic region, meanwhile, the line draft structure is kept complete, and the requirement for rapid personalized drawing can be met.

The present invention discloses a method and terminal for converting MRI images and CT images based on deep learning. The method comprises the following steps: obtaining a training MRI image and a training CT image, performing N4 bias correction and histogram matching on the training MRI image and the training CT image to obtain a preprocessed training MRI image and a preprocessed training CT image; training and verifying an initial fully convolutional neural network model based on the preprocessed training MRI image and the preprocessed training CT image to obtain a final fully convolutional neural network model; obtaining an image to be converted, and inputting the image to be converted into the final fully convolutional neural network model to obtain a synthetic image corresponding to the image to be converted, thereby improving the accuracy of the conversion between MRI images and CT images.

The application provides a style transfer strong similarity-based generation method, device, equipment and medium, the method comprising: S10, redrawing a style reference map, outputting a clear style map; S20, inputting the clear style map into a monocular depth estimation model, outputting a single-channel depth map; S30, inputting the single-channel depth map into an enhanced image generation model for structure optimization, generating a redrawing sketch; S40, using a visual feature extraction model and a deep learning model to perform style fusion and rendering on the redrawing sketch, generating a preliminary style map; S50, pre-training a LoRA model, using the LoRA model to adjust the preliminary style map, outputting an intermediate style map; S60, after upsampling the intermediate style map, performing block optimization, outputting an optimized style map; S70, inputting the optimized style map into a Lab color space, performing histogram matching, and generating a final style map.

The invention relates to the technical field of computer vision and automation, discloses a robot medicine recognition method based on histogram feature recognition, and aims to solve the problem of medicine recognition of an automatic dispensing robot in a pharmacy under the conditions of limited computing power and lower camera resolution. According to the method, by combining ORB feature matching and color histogram matching, efficient and accurate recognition under the low computing power requirement is achieved, and the method is suitable for embedded equipment. According to the histogram feature recognition-based robot medicine recognition method provided by the invention, the limitation of a deep learning technology in the aspects of computing power, real-time performance and data labeling cost is overcome, a stable and reliable medicine recognition solution is provided for an automatic medicine dispensing robot in a pharmacy, and the safety and efficiency of medicine dispensing are remarkably improved.

The application discloses a kind of micro-light image enhancement methods, including obtaining target micro-light image;Based on gamma correction, RTV correction and histogram matching scheme, the structure diagram and texture diagram of target micro-light image are calculated;According to the structure diagram and texture diagram obtained, based on Retinex model and structure / texture guide scheme, the brightness diagram and reflection diagram of target micro-light image are calculated;The obtained brightness diagram is adjusted and adaptively corrected;Based on RTV correction scheme, the enhanced image of target micro-light image is calculated to complete the image enhancement of target micro-light image.The application also discloses a kind of mine image enhancement method comprising the micro-light image enhancement method.The application not only realizes the enhancement of micro-light image and mine image, but also has higher reliability, better accuracy and better enhancement effect.

The present disclosure relates to an image processing method, apparatus, electronic device, and medium, and relates to the fields of image processing, satellite image processing technology, etc. The method includes: obtaining an image to be processed, where the image to be processed is an image that needs to be color-adjusted; dividing the image to be processed into blocks according to N rows and M columns to obtain respective block images, determining the color deviation values of the respective block images to obtain respective color deviation values, where N and M are positive integers; based on the color deviation values corresponding to the respective block images, performing color adjustment on the image to be processed by means of histogram matching, and performing an elimination operation on the splicing edges of the color-adjusted image to obtain a grayscale image; adjusting the size of the grayscale image, and inputting the grayscale image with the adjusted size into a style transfer neural network to perform style transfer to obtain a target image. The present disclosure realizes efficient and automated image color adjustment and improves the efficiency.

The invention relates to the technical field of biological detection, and discloses an intestinal digestion flora sampling and classification detection method and system, and the method comprises the steps: analyzing a feature image, extracting high-frequency texture features and low-frequency texture features, obtaining a frequency domain feature parameter set, extracting an RGB color histogram from an original flora microscopic image, and obtaining a frequency domain feature parameter set; obtaining a color feature parameter set; according to the method, self-adaptive double-threshold edge fusion and three-dimensional skeleton topology analysis are adopted, so that the quantification precision of morphological characteristics is greatly improved, thallus contour fracture and measurement errors are reduced, and the reliability of a result is improved; secondly, the dynamic adaptability of fractal feature extraction is improved, the classification requirements of new flora can be effectively met, and the classification accuracy is improved; besides, through HSV channel strengthening and multi-channel histogram matching, color feature quantification is objectified, subjective errors in traditional manual interpretation are eliminated, and color feature recognition is more accurate.

The application discloses a high-resolution satellite image urban area shadow-removed ARD production method and device, and belongs to the technical field of remote sensing image processing. The method comprises the following steps: firstly, screening out images covering urban areas; performing radiation calibration on high-resolution satellite images covering urban areas to obtain apparent radiance images; based on the apparent radiance images, performing preliminary shadow detection by using a pre-trained deep learning model; using a near-infrared band of an atmospheric-corrected ground reflectivity image, performing false detection elimination on the preliminary detection result by adaptive threshold segmentation to obtain a final shadow mask; performing histogram matching processing on the shadow area based on the shadow mask on the atmospheric-corrected ground reflectivity image to realize shadow removal; and finally, outputting the shadow-removed ARD product. The application significantly improves the shadow detection precision and the quantitative reliability of the ARD product, and is suitable for large-scale ARD production of high-resolution satellite images in urban areas.

The present invention discloses a method for detecting building changes based on a multi-scale twin network, comprising: performing color space conversion on two images containing building changes to be detected; performing brightness histogram matching; performing chromaticity histogram matching; constructing a multi-scale twin network model and training it; using the trained model to perform multi-scale feature extraction on the image to obtain a feature map; combining the feature map with the trained model to obtain a difference map, thereby completing building change detection based on a multi-scale twin network. In order to solve the problem of false detection caused by shadows on buildings and other landmark coverings, the present invention adopts a double histogram matching method to eliminate the differences that affect chromaticity and brightness, and at the same time introduces a multi-scale feature extraction model to reduce the scale differences between the changed buildings. This enables preliminary screening of renovation and expansion sites for important targets such as buildings, and assists in detailed and precise judgment of changed areas.

The invention provides an inclined image dodging processing method and device, and is applied to the technical field of aerial remote sensing. The method comprises the following steps: carrying out observation geometric calculation and illumination geometric calculation according to imaging parameters and position attitude data of an inclined camera; determining atmospheric influence parameters from the atmospheric correction parameter lookup table according to a calculation result, determining theoretical radiance of each pixel in the original inclined image based on the atmospheric influence parameters, determining standard brightness, and unifying a brightness reference of the theoretical radiance based on the standard brightness; calculating a cosine quadruple proportion coefficient of an actual correction angle of oblique observation of the camera, and correcting the theoretical radiance of each pixel based on the cosine quadruple proportion coefficient; and converting the processed original inclined image and the reference image from a first color space to a second color space, performing histogram matching on each channel, and then turning back to the first color space to obtain a uniform light normalized image.

The invention discloses an image sequence flicker elimination method and system based on clustering and multi-scale histogram matching, and relates to the technical field of image processing, and the method comprises the steps: carrying out the denoising processing of an image through employing a non-local mean value denoising algorithm, obtaining a denoised image sequence, calculating the histogram features, and carrying out the calculation of the histogram features; grouping the images in the de-noised image sequence by adopting a K-Means clustering algorithm so as to determine a category label of each frame of image; for the de-noised image sequence, taking an image of which the category label is a standard category as a reference, performing multi-scale histogram matching on an image of which the category label is an abnormal category, and performing weighted fusion on optimized images under all scales through a Gaussian kernel function to obtain an optimized image sequence; and for the optimized image sequence, carrying out time sequence smooth transition processing at the image junction of different types of labels by adopting a cross gradual change technology so as to obtain a final image sequence. The flicker of the image sequence can be effectively eliminated, and the visual experience is improved.

The application discloses a method and system for automatically correcting the angle of head data in a medical image, and the method comprises the following steps: setting a reference picture, inputting a picture sequence to be corrected, wherein the input picture sequence to be corrected is a medical picture or picture sequence obtained after CT or nuclear magnetic resonance scanning and reconstruction of a patient; searching for a similar slice: using a histogram matching method or other algorithms based on a deep neural network to search for a similar slice in the input sequence which is most similar to the reference picture; head correction: registering the found similar slice picture with the reference picture to obtain correction parameters, including a rotation angle and horizontal offset in x and y directions, and then processing the picture sequence to be corrected to realize head correction. The application automatically corrects the image, automatically corrects the position of the head of the patient in the input data by using an image registration method, and replaces three-dimensional direct registration by a two-dimensional registration method, so that the calculation amount is greatly reduced and the accuracy is increased.

The application discloses a kind of based on 4D millimeter wave radar / inertial sensor tightly coupled navigation positioning method, comprising: the original point cloud is based on the dynamic point rejection of Doppler consistency, obtain first point cloud, then carry out noise point rejection and obtain second point cloud;Weighted Doppler velocity factor residual corresponding to single frame point cloud is constructed based on Doppler velocity weight;Adjacent two frames of second point cloud are histogram matched, obtain interframe point pair set, and interframe point pair factor residual is constructed based on interframe point pair set;Joint weighted Doppler velocity factor residual, interframe point pair factor residual and inertial pre-integral factor residual, construct tightly coupled nonlinear factor graph optimization model, and the navigation positioning information of carrier is obtained by executing nonlinear factor graph optimization.The application is applied to navigation positioning field, faces extreme dynamic scene, the characteristics of dynamic, static target are jointly analyzed, and positioning precision and robustness in dynamic scene are significantly improved.

The present invention relates to an image restoration method based on an underwater non-uniform incident light model, belonging to the field of image restoration. It includes the following steps: Step S1, perform wavelet decomposition on the underwater image to obtain the lowest-frequency subband and each high-frequency subband; Step S2, based on the underwater non-uniform incident light model, perform scattering suppression on the lowest-frequency subband to obtain the processed lowest-frequency subband; Step S3, perform noise suppression and detail enhancement processing on each high-frequency subband to obtain the processed high-frequency subband; Step S4, perform wavelet reconstruction on the processed lowest-frequency subband and the processed high-frequency subband to obtain a preliminary restored image; and Step S5, perform dynamic range stretching and histogram matching on the preliminary restored image to obtain the restored image. Therefore, the restoration method provided by the present invention can effectively improve the clarity and contrast of the image, restore more natural color information, and has better comprehensive performance and broad application prospects.

The present invention discloses a method and system for improving the geometric accuracy correction of historical geological maps. The present invention obtains high-resolution panchromatic images and medium-resolution multispectral images corresponding to historical geological maps, performs geometric correction on the high-resolution panchromatic images with standard remote sensing images as a reference, and then performs geometric correction on the medium-resolution multispectral images with the corrected high-resolution panchromatic images as the reference images; finally, histogram matching and fusion are performed on the single-band intensity images of the high-resolution panchromatic images and the medium-resolution multispectral images to generate historical surface reference color images or further three-dimensional analysis for geometric correction and patch content correction of historical geological maps. The present invention effectively solves the difficult problem of high-precision geometric correction and patch content verification and correction of historical geological maps due to their long age and large surface changes by reconstructing high-precision historical reference scenes, thereby obtaining high-quality historical geological maps, and can perform spatiotemporal change analysis and application with current geological maps.

The application discloses a kind of mass remote sensing image automatic color correction methods of sub-meter satellite.Belongs to the technical field of mass sub-meter satellite remote sensing image automatic color correction, specifically relates to a kind of mass remote sensing image automatic color correction methods of sub-meter satellite technical field, by training improved ResNet50 model, obtain mass sub-meter high-resolution paired data, then combined with histogram matching method, realize mass sub-meter satellite remote sensing image automatic color correction.The method comprises the following steps: obtaining pre-correction sub-meter remote sensing image, and performing histogram matching on pre-correction sub-meter remote sensing image;Pre-process the pre-correction sub-meter remote sensing image and corresponding RGB histogram;ResNet50 model is constructed and improved: after the full connection layer of ResNet50 model, increase full connection module;Sub-meter remote sensing image is corrected by ResNet50 improved model.

This application provides an anti-interference texture enhancement localization method, system, and medium suitable for low-texture visual scenes. The method includes: acquiring an original image; preprocessing the original image; extracting the texture response of the preprocessed image in different directions and frequency dimensions based on multi-scale Gabor filtering to obtain multiple texture response maps; dividing the preprocessed image into multiple image partitions based on a partitioned LED array and calculating the local gradient contrast of each image partition; independently adjusting the light source brightness of different image partitions to obtain an illumination-optimized image; performing feature fusion processing on the multiple texture response maps and the illumination-optimized image to extract feature points for visual localization; improving the consistency of images acquired at different time periods by normalizing the frequency response of the original image, histogram matching, and adaptive brightness restoration; and improving the local contrast by using a partitioned LED array with spatial illumination modulation to improve visual localization accuracy.

This invention discloses an adaptive enhancement method for low-light images based on physical prior guidance and spatiotemporal decoupling, belonging to the fields of computer vision and autonomous driving perception technology. The method first converts the original low-light image to YUV space to extract the brightness channel and performs inverse mapping. A pseudo-dehazing physical model is used to inversely solve for the transmittance map as the structural prior matrix. Subsequently, a mask decomposition network decouples the original image features into a reflection component containing local texture and an initial illumination component containing global brightness distribution. This invention constructs a dual-branch parallel enhancement architecture: the first branch uses an enhanced Transformer network for detail restoration and introduces a color histogram matching loss function to ensure color fidelity; the second branch uses an adaptive Gamma U-Net to predict pixel-level gamma parameter matrices for brightness compensation and suppresses overexposure through a regularized loss function with a range penalty term. Finally, the enhanced image is reconstructed based on Retinex theory.