1412 results about "Contrast ratio" patented technology

The invention relates to the technical field of image data processing, in particular to a cable surface defect detection method and system based on machine vision. The method comprises the following steps: acquiring a surface image of a cable, and converting the surface image into a grayscale image; determining the local complexity of each pixel point; obtaining a plurality of areas of the grayscale image, performing complexity determination, and dividing each area to obtain a plurality of windows of each area; determining a contrast limit threshold value of each window; performing image enhancement by using a CLAHE algorithm to obtain an enhanced grayscale image; and carrying out cable surface defect detection on the enhanced grayscale image by using a defect detection algorithm. According to the method, the CLAHE parameters are adaptively adjusted based on local complexity, the window size and the contrast threshold are dynamically determined in combination with gray and gradient information, discontinuity is corrected and eliminated through boundary similarity, and the quality and reliability of a cable defect detection image are improved.

The invention relates to an underwater image enhancement method based on wavelet Mama, which aims at the problems of color shift, low contrast ratio and fuzzy details of an underwater image, extracts shallow layer features of an underwater low-quality image, inputs the shallow layer features into a multi-scale coding network, and performs joint enhancement on low-frequency color information and high-frequency detail features by using a wavelet Mama unit. Global semantic features are obtained by combining down-sampling layer-by-layer compression, and global modeling of color offset correction and contrast enhancement is completed; the spatial resolution is recovered through up-sampling, and reconstruction and enhancement of texture details and color information are realized in combination with jump connection and a wavelet Mama unit; and finally, the features are mapped to an image domain and fused with input image residual errors, and an enhanced underwater image with natural color, clear texture and balanced contrast is generated. According to the method, frequency domain-space domain joint feature modeling is realized through the wavelet Mama unit, and the color authenticity, the structural definition and the detail perceptibility of the underwater image are remarkably improved.

The invention provides a modified metal surface defect detection method and device and a medium, and the method comprises the steps: obtaining a multi-angle reflection image sequence of a modified metal surface under the irradiation of a multi-spectral light source, and generating a defect sensitive parameter set based on a preset modified metal material characteristic database and in combination with the spectral reflectivity distribution information of the multi-angle reflection image sequence; performing cooperative feature enhancement on the multi-angle reflection image sequence and the defect sensitive parameter set, and enhancing the feature contrast of a defect area and a normal area through weight distribution to obtain an enhanced defect feature set; joint anomaly detection of a spatial domain and a spectral domain is carried out on the enhanced defect feature set, a potential defect region of the modified metal surface is obtained through identification, and a defect region feature descriptor is generated; and performing defect morphological quantitative analysis according to the defect region feature descriptors, and determining the type, position and severity level of the modified metal surface defect. According to the invention, the comprehensiveness and reliability of a defect detection result can be improved.

The invention relates to the technical field of medical image detection, and discloses a medical image quality detection method based on image processing. The method comprises the following steps: acquiring medical image data to be detected, wherein the medical image data comprises a multi-modal scanning image sequence and corresponding acquisition parameters; the medical image data are preprocessed, standardized image data are generated, and the standardized image data comprise unified parameters of spatial resolution, gray scale range and noise level; extracting structural features of the standardized image data, wherein the structural features comprise tissue boundary gradient distribution, texture consistency and local contrast information; constructing a quality evaluation model according to the structural features, wherein the quality evaluation model analyzes a mapping relationship between the structural features and preset quality indexes through a dynamic convolutional network; and outputting a quality defect detection result based on the quality evaluation model, wherein the quality defect detection result marks an image region with artifacts, fuzziness or distortion.

The invention discloses a single image super-resolution reconstruction method and system based on wavelet transform and cross-domain feature fusion. According to the method, firstly, a low-resolution RGB image is mapped to a high-dimensional feature space through a shallow feature extraction module; performing up-sampling and discrete wavelet decomposition on the features by using a wavelet feature mixing module to obtain multi-band features; low-frequency and high-frequency depth features are respectively extracted through a double-branch structure, cross-domain fusion is realized by means of a deformable cross attention mechanism, and the feature expression ability is enhanced in combination with residual connection; and finally, reconstructing a high-resolution image through convolution, up-sampling and regularization processing. In the training process, a pixel-level loss function is adopted to optimize network parameters, the multi-frequency-domain feature sensitivity is effectively improved, texture and structure information is balanced, the image contrast, definition and structural integrity are improved, and high-quality real-time super-resolution reconstruction can be achieved.

The invention belongs to the technical field of image processing, and particularly relates to a monitoring image defogging enhancement method in a high-dust environment of a mineral separation site, which comprises the following steps of: performing smooth denoising on an original image by using weighted guided filtering, and obtaining global atmospheric light through a quadtree subdivision method; constructing a same-color heterogeneous discrimination index combining a spectrum similarity factor and a texture confidence factor for distinguishing dust and ore with similar colors; calculating a pixel-level dynamic defogging coefficient based on the discriminant index, and obtaining an adaptive transmissivity in combination with dark channel prior; and finally, restoring the image by using an atmospheric scattering model and carrying out contrast-limited adaptive histogram equalization processing. According to the method, the problem of misjudgment caused by the fact that the colors of the ore and the dust on the ore dressing site are similar is effectively solved, powerful defogging of the dust area and detail reservation of the ore area are achieved, and the definition and the contrast ratio of the monitoring image are improved.

The invention relates to the technical field of image processing, and discloses a defect detection method and device for a wafer chip. The method comprises the following steps: preprocessing an original wafer image, wherein the preprocessing comprises at least one of the following items: contrast enhancement, noise suppression and smoothing, sharpening enhancement and normalization processing; dividing the preprocessed wafer image into a plurality of chip areas, and extracting a feature vector of each chip area by using a convolutional neural network to form an initial node feature matrix; based on the spatial position relationship between the chips, constructing a spatial adjacency graph of the wafer; and inputting the initial node feature matrix and the spatial adjacency graph into a defect detection model, and outputting a wafer-level defect detection result. According to the method, comprehensive modeling of defects in spatial distribution and associated feature levels can be realized, and the accuracy and robustness of detection are improved.

The invention discloses an image edge feature enhancement correction fusion method based on a guide filter, and aims to solve the problems that detail features of a low-illumination visible light image and an infrared image are not obvious, focusing edge information is not clear, and registration of a multi-focus image is wrong. The invention provides an edge feature enhancement correction fusion method based on a guide filter. In the illumination enhancement stage, a visible light image is divided into a base layer and a detail layer through a guide filter, and the image contrast and detail information are enhanced. For an infrared image, an infrared background is reconstructed by using a quadtree decomposition and Bezier interpolation method, unclear focusing edge feature information is extracted, and the visibility of image details is enhanced. And finally, reconstructing the two processed images by using a multi-scale weighted gradient method, and performing fusion by calculating large-scale and small-scale weight scales.

The invention provides a stone gradation automatic detection method based on image processing, belongs to the technical field of image processing, and designs a two-dimensional convolution kernel capable of enhancing features according to the features that small-particle stones are bright in center, dark in edge and approximate to a circle by acquiring a gray image and a background image of a stone field through a video stream. Convolution operation is carried out on the image to improve the contrast ratio of the stone and the background, then an accurate binarized image is obtained through double-threshold segmentation and background difference processing, in order to further separate the adhered particles, the particle center is positioned by adopting distance transformation, and effective segmentation is carried out by combining a watershed algorithm. And according to the extracted particle contour, geometric parameters are calculated and the mass is estimated, so that a stone grading curve for evaluating the filling quality is automatically generated. According to the invention, rapid and non-contact automatic analysis of rockfill material grading is realized.

The invention relates to the technical field of intelligent control, and discloses an ultra-low power consumption miniature LED visible light intensity self-adaptive regulation and control method and system, and the method comprises the steps: obtaining an analog light intensity signal, and carrying out the smoothing processing of the analog light intensity signal, and obtaining an environment light intensity value; calculating a light intensity change rate, and if the light intensity change rate exceeds a dynamic threshold value, marking an environment state and outputting a light intensity difference value; generating a preliminary adjustment instruction according to the state matching control parameter and the difference value quantization mapping, and calculating target light intensity in combination with the basic brightness; after executing the instruction, calculating actual and target deviation, and iteratively correcting to obtain a light intensity control signal; if the power consumption exceeds the limit, optimizing the signal amplitude; the input driving circuit analyzes the synthesized pulse current, excites pixels to emit light and gathers to obtain adjustment data; and if the visual contrast is lower than a threshold value, the change rate is adjusted again to realize closed-loop refining, and the light intensity adjustment accuracy is improved. The method can improve the real-time sensing capability of ambient light.

The invention belongs to the technical field of image processing, and particularly relates to a composite material ultrasonic scanning image defect feature extraction method and system, and the method comprises the steps: obtaining an ultrasonic scanning image of a composite material, and carrying out the filtering processing; constructing a gradient outer product matrix of neighborhood pixel points of the pixel points and accumulating to obtain a local structure matrix; performing characteristic decomposition on the local structure matrix to obtain a characteristic value, and calculating a structure coherence factor according to the characteristic value; coupling potential energy is constructed in combination with a gray value and a structural coherence factor, and a low-gray and disordered defect signal is highlighted through nonlinear gain; and performing region segmentation by using the high-coupling potential energy points as anchor points, and extracting defect blocks. According to the method, the problem of low-contrast defect leak detection under the strong texture background is effectively solved by utilizing the essential difference between the background texture and the defect in the physical topology, and the defect feature extraction precision is remarkably improved.

The invention provides a multi-scale pyramid weighted fusion underwater image enhancement method based on double prior, and the method comprises the steps: obtaining a degraded underwater image, and carrying out the global and local cooperation body color calibration of the degraded underwater image; decomposing the color correction image into a base layer, a detail layer and a noise layer by adopting a variational decomposition algorithm; performing spectral prior and transmissivity loss constraint on the base layer image to obtain a defogged image; fusing the detail layer image and the noise layer image to obtain a filtered image; performing enhancement processing on the filtered image by adopting a nonlinear mapping and contrast enhancement strategy to obtain an enhanced image; performing multi-level feature integration and reconstruction on the defogged image and the enhanced image by adopting a multi-scale pyramid adaptive weighted fusion method to obtain an underwater image with natural color and high visual definition; according to the method, the traditional image processing and variational optimization thought is combined to effectively correct the color deviation of the degraded underwater image, the image contrast and the detail definition are improved, and the visualization effect of the underwater image is improved.

The invention discloses an abdominal CT image multi-view fusion method based on HU physical characteristic guidance, and relates to the technical field of medical image processing. The method comprises the following steps: firstly, constructing a three-dimensional HU volume, generating a semantic mask covering the whole HU range as physical prior, and obtaining two-dimensional slice sequences in the axial direction, the coronal direction and the sagittal direction through three-view projection; on the basis, multi-scale deformable alignment, HU-perceived deformation field fine correction, cross-view attention fusion and regional differentiation decision fusion are sequentially carried out, HU-perceived high-frequency residual enhancement and local contrast self-adaptive processing are carried out on two-dimensional slices, and finally a denser CT slice sequence is generated. According to the method, HU physical partition constraint is introduced in the multi-view alignment and fusion process, abnormal deformation and high-density structure distortion of a gas region are effectively inhibited, and geometric consistency and visual stability of an abdominal CT image in the interpolation reconstruction process are improved.

The invention provides a method and a system for restoring a bamboo-strip character image based on multi-granularity feature guidance, and innovatively designs a coarse-fine two-stage restoration network and end-to-end multi-task loss joint training aiming at the problems of structure-texture confusion, non-uniform degradation, low contrast ratio and the like of the bamboo-strip image. In the coarse repair stage, a font texture and structure double-reconstruction sub-network is used for separating semantics from a source; in the fine repairing stage, multi-scale dynamic range distribution diagram self-attention (Mdma) is provided, pixels are dynamically classified according to degradation intensity, and long-short range dependence joint modeling is achieved; an adaptive mask is designed to sense pixel shuffling downsampling (Ampd), sampling is guided by mask confidence, damage position information is kept, and artifacts are inhibited. Five mainstream methods are compared on a homemade 313 bamboo strip single word data set, the PSNR, the SSIM and the FID are optimal under 0-60% irregular masks, visual evaluation of real missing samples is natural in texture, the structure is complete, and the readability of the bamboo strip characters and the subsequent recognition accuracy are effectively improved.

The invention relates to an underwater image quality improvement method based on adaptive color correction and contrast enhancement, which comprises the following steps: firstly, designing an adaptive correction strategy to carry out channel compensation on an underwater image to obtain an underwater image after color correction; a brightness channel is extracted, and a color interference layer is filtered out, so that global backscattered light is estimated; and gradient domain detail enhancement is carried out, defogging processing is carried out on the base layer of the underwater image after color correction, brightness adjustment is carried out on uneven illumination, and an enhanced image is obtained. According to the invention, by compensating the attenuation of the underwater environment to the image information, the color distribution balance of the three channels is realized, so that the color channels of the underwater image are naturally distributed; a plurality of prior knowledge of the back scattering light is fused, a Gaussian filter of an adaptive standard deviation is constructed, a color interference layer is separated, and the back scattering light can be accurately estimated without being interfered by a white object and a highlight area; therefore, multi-target-oriented contrast enhancement is realized to improve the overall visibility of the image.

The invention discloses an SLAM front-end optimization method based on brightness grading and gradient constraint, and relates to the technical field of computer vision and vision SLAM. In order to solve the defects that adaptive grading enhancement and parameterization control based on illumination types are lacked in the prior art, and feature point quality, spatial distribution uniformity and front-end real-time performance are difficult to consider in a high-frequency input scene, the invention provides a comprehensive brightness grading and feature optimization scheme. The method comprises the following steps: firstly, calculating the average brightness of an input image, dividing the image into a dark light type, a normal type and an overexposure type, and respectively adopting gamma correction, contrast limited adaptive histogram equalization and inversion enhancement strategies for different types to realize illumination adaptive enhancement; then screening high-quality feature points with significant local curvature changes; and updating the detection area. According to the method, the feature stability, the matching precision and the real-time performance of the SLAM front end in a complex indoor environment are remarkably improved, and the method is suitable for a self-localization and mapping system.

The invention belongs to the technical field of deep learning and image processing, and discloses an underwater image enhancement method based on tensor learnable prior. By designing learnable prior based on a Tensor Train kernel, explicit modeling of the underwater degradation law is realized. The four Tensor Train kernels correspond to the height, the width, the channel and the image block modality respectively, so that the system can simultaneously describe the vertical structure change, the horizontal scattering, the spectral absorption difference and the regional scale attenuation, thereby obviously enhancing the description capability for the underwater multimode degradation. The Tensor Train kernel is dynamically generated based on the kernel prediction sub-network, the prior structure of the method can be adaptively adjusted along with the input image, and the stability and generalization ability of the enhancement result are improved. According to the method, color shift is effectively corrected and scattering blur is inhibited while the structure edge is kept, so that the enhancement process is changed from traditional black box type mapping to physical consistency structured reasoning, and more natural colors, higher contrast and better detail recovery effects are obtained in a complex turbid environment.

The invention discloses an underwater scene three-dimensional reconstruction system and method based on 3D Gaussian sputtering, and belongs to the technical field of computer vision and three-dimensional reconstruction. The invention provides a feature extraction and enhancement method based on an AWDesc descriptor and a lightweight FeatureBooster network, and supports detection and description of key points of a fuzzy and weak texture area in a multi-view underwater image. According to the method, the self-enhancement and cross-enhancement strategies are combined, the discriminability and context consistency of descriptors are improved, and the mutual nearest neighbor and RANSAC algorithm are utilized to construct stable feature matching pairs, so that the mismatching rate is effectively reduced, and the accuracy of camera attitude estimation and sparse point cloud construction is improved. According to the method, the image enhancement network based on the cross Transform and the mixed contrast learning mechanism is adopted, and the ubiquitous problems of color distortion, fuzzy degradation and insufficient contrast of underwater images are effectively relieved. According to the method, a covariance correction mechanism and an underwater perception loss function are introduced into a 3D Gaussian sputtering three-dimensional modeling framework, and adaptive adjustment of Gaussian kernel scale, density and structural directivity is realized.

The invention discloses a low-light image enhancement method based on multilevel feature fusion. The low-light image enhancement method comprises the steps of acquiring a data set, dividing the data set, extracting features, constructing a synchronous multi-scale network, training the synchronous multi-scale network and testing the synchronous multi-scale network. According to the synchronous multi-scale low-light image enhancement method in combination with the Laplacian pyramid, the input image is processed in parallel by adopting a double-path structure: the preliminary enhancement image is obtained through the local-global convolutional neural network, and the detail and texture information of the image is enhanced based on the Laplacian pyramid decomposition network. A multi-scale network is adopted to process images in scenes of deblurring, defogging, rain removal, low light enhancement and the like, and details and features are extracted in a layered manner, so that the definition, color and contrast ratio of the images are effectively improved. Comparison experiments prove that the method has the advantages that noise is effectively suppressed, and remarkable effects are achieved in the aspects of detail recovery and color restoration. The method is suitable for image enhancement processing under various complex illumination conditions.

The invention discloses a single image defogging method based on block-by-block nonlinear brightness prior, which belongs to the technical field of image processing, and comprises the following steps of: dividing a fog-containing image into local blocks, calculating the average brightness of the blocks, and constructing prior block-by-block monotone increasing nonlinear mapping to represent the corresponding relationship between the brightness of fog-containing blocks and the brightness of clear blocks; an atmospheric scattering model is combined, an atmospheric light vector is modeled into a vector, the vector and a PPWF form a parameterized recovery model, three scalar parameters are taken as a core, an optimal parameter is obtained through multi-target joint optimization, alternate optimization and golden section search, and finally a defogged image is generated. The method has the advantages of few parameters, low complexity and no need of training data, the definition and global contrast of far and near scenery can be remarkably improved while the image structure is maintained, and halo, excessive enhancement and color cast are effectively inhibited; the method has good robustness for different fog densities and illumination conditions, and is suitable for real-time and embedded defogging application of single-channel or multi-channel images.

The invention relates to the technical field of semiconductor detection, and discloses a method and a system for detecting chips on the surface of a semiconductor wafer. The method comprises the following steps: acquiring a high-resolution image of the surface of a wafer, and separating a defect candidate region set from a reference background region through noise filtering and contrast equalization processing; extracting defect areas to be identified one by one, and accessing the defect knowledge graph to obtain potential defect types; performing multi-feature fusion on the potential defect type and the reference background region, generating a defect semantic feature vector through a context sensing encoder, and analyzing the vector to judge the actual defect type; and processing all the candidate areas and then outputting a defect detection report. According to the method, the distinction degree of defects and backgrounds is enhanced, the defect judgment range is narrowed, similar defects are accurately recognized, missing detection and false detection are reduced, the detection efficiency and accuracy are improved, and reliable technical support is provided for wafer production quality control.

The invention belongs to the technical field of image processing, and particularly relates to an MBR flat membrane surface physical damage visual identification method and system, and the method comprises the steps: carrying out the low-pass filtering of a microscopic gray image, so as to melt a micropore background; calculating a texture variation index based on the outlier degree of the gray values of the neighborhood pixel points and the neighborhood gray dynamic contrast gain; determining a damage aggregation weight by using a space attenuation accumulation value, and eliminating isolated noise points; the texture variation index and the damage aggregation weight are fused through a self-adaptive gating function, and damage confidence is generated; and finally marking a connected damage region based on statistical threshold binarization and morphological processing. According to the method, dense micropore interference can be effectively inhibited, and the physical damage identification precision is improved.

The invention relates to the technical field of display screen defect visual inspection, in particular to an LED display screen defect visual inspection method and system, and the method comprises the following steps: collecting an image of an LED display screen; performing gamma correction and noise suppression on the image of the LED display screen to obtain a preprocessed image; local contrast enhancement and adaptive threshold segmentation are applied to the preprocessed image, and candidate abnormal areas are identified; for the candidate abnormal region, extracting at least one dimension feature which comprises at least one of brightness, color, texture, edge gradient and spectral response; processing the at least one dimension feature based on a preset discrimination rule so as to distinguish real microdefects, optical artifacts and digital artifacts of the LED display screen; and outputting the type, the position and the confidence of the defect obtained by distinguishing. The detection accuracy and reliability are improved.

The invention provides a visual mapping method in a low-light environment, and relates to the technical field of visual mapping, and the method comprises the steps: separating a brightness channel from an input image, dividing the brightness channel into a plurality of tiles, calculating the local statistic of each tile to adaptively generate a contrast amplitude limiting parameter and an enhancement parameter vector, carrying out the CLAHE enhancement of the brightness channel based on the parameters, and obtaining a visual mapping result. After an image is reconstructed, candidate points in one-to-one correspondence with existing map feature points are extracted, comprehensive confidence scores of the candidate points are calculated, and whether the candidate points are adopted as new map points or not is determined by judging whether enhancement parameter vector distances between the candidate points and the corresponding map points are lower than a dynamic threshold value or not and combining the confidence scores. And finally, dynamically optimizing and enhancing parameters by the system according to the matching survival rate of each tile feature point, continuously improving the mapping quality and stability through iterative updating, and realizing robust visual mapping in a low-light environment.

The invention discloses a liquid crystal display device for improving contrast based on movable light guide lattice points and a light guide layer and a control method of the liquid crystal display device. The device comprises a backlight module, a dynamic light guide layer, a liquid crystal display panel and a control system. The dynamic light guide layer is packaged by two transparent substrates to form a sealed cavity, the cavity is filled with transparent base liquid and movable micro units with different refractive indexes, and transparent electrode arrays are arranged on the inner surfaces of the substrates. The control system controls the electrode array to generate a corresponding electric field according to brightness and darkness distribution of an input image signal, drives the micro-units to move in the base liquid and gather to the position below an image brightness field area so as to enhance light output, and meanwhile enables the micro-units to be dispersed below a dark field area so as to reduce light leakage. According to the invention, a static light guide structure is changed into a dynamic programming system, and the real-time pixel-level matching of backlight distribution and display content is realized, so that the contrast ratio is remarkably improved, the halo is inhibited, and the power consumption is reduced. The invention also protects the core component of the dynamic light guide layer and the corresponding control method.

The invention relates to the technical field of tennis recognition, in particular to a tennis recognition model based on adaptive illumination variation, which obtains an image brightness histogram by inputting an image, recognizes a region with uneven illumination distribution, and executes local contrast enhancement and global modeling, thereby effectively enhancing details of a target region, and improving the recognition accuracy. And the detectability of the tennis ball under weak light and complex light is improved. In combination with multi-scale feature extraction and classification identification, robust detection of tennis balls in different sizes and motion states is realized, and the method is suitable for multi-angle and light source interference scenes. And meanwhile, trajectory prediction and restoration are performed on detection results in continuous image frames by adopting a DeepSORT algorithm, so that the space-time continuity of target tracking is guaranteed, and target loss caused by detection interruption is avoided. The whole scheme integrates image enhancement, detection and tracking mechanisms, improves the accuracy and stability of tennis recognition under a dynamic illumination condition, and is suitable for a complex tennis outdoor competition environment.

The invention discloses a multi-contrast magnetic resonance image reconstruction method and device based on detail contour feature perception. The method comprises the following steps: acquiring a target modal initial image and an auxiliary modal initial image; constructing an iterative network formed by alternately cascading image domain reconstruction units and data consistency layers, wherein each image domain reconstruction unit comprises an encoder and a decoder; in the first iteration, target modal initial images and auxiliary modal initial images are spliced and then input, an encoder extracts shared features firstly, then global contour features and high-frequency detail features are separated in parallel, and potential features are obtained through collaborative fusion; the decoder takes the potential features as input and outputs an image domain preliminary reconstruction result; the data consistency layer transforms the preliminary result into a k space, performs consistency correction on the preliminary result and a target modal sampling point, and then inversely transforms the preliminary result back to an image domain to complete one iteration; and splicing the current output and the auxiliary modal initial image again, inputting the spliced image into a next round of iteration, and repeating the process until a preset number of times to obtain a final target modal magnetic resonance image.

The invention provides a chromosome image enhancement method and system based on semantic guidance. The method comprises the following steps: S1, preprocessing; s2, outputting a deep-band probability graph, a grey-band probability graph and a shallow-band probability graph through a semantic segmentation network composed of a lightweight encoder and a multi-scale decoder; s3, implementing differential layered enhancement according to a band type: adopting local adaptive histogram equalization for a deep band, adopting central axis constraint bilateral filtering for a gray band, adopting dynamic threshold truncation and gamma correction for a shallow band, and performing weighted fusion for a transition region according to probability; s4, carrying out structure strengthening, wherein the structure strengthening comprises centromere local sharpening, stripe phase alignment, edge sensing super-resolution and overlapping region separation; and S5, performing quality evaluation based on the deep band integrity, the band stripe contrast uniformity, the SSIM and the noise density, and triggering adaptive re-enhancement if necessary. According to the scheme, the contrast ratio and details are remarkably improved while the stripe structure and the position relation are kept, and the method has the advantages of light weight, interpretability and cross-sample robustness and is suitable for being integrated into an automatic karyotype analysis process.

The invention belongs to the technical field of semiconductor display, particularly relates to a backlight and display drive cooperative control LED module system, and aims to solve the problems of brightness distortion, halo effect and energy efficiency degradation caused by disconnection of backlight and pixel control in high dynamic range display. The system generates adaptive backlight distribution through image semantic analysis and illumination field reconstruction, and realizes stable light output in combination with dynamic cutting compensation and temperature feedback; and the display driving end performs gray scale feedforward correction according to the backlight state, and introduces a cross-domain error back propagation mechanism to realize frame-level online calibration. And the dynamic response and the processing real-time performance are further optimized through motion vector sensing and double-phase time sequence scheduling. According to the system, the contrast ratio is increased to more than 100000: 1, the halo radius is reduced to 40% of that of a traditional scheme, the power consumption is reduced by 18%, and the visual experience and the operation stability are remarkably improved.

The application relates to the technical field of image recognition, in particular to a bearing surface defect automatic detection system based on image recognition, which comprises an image acquisition and preprocessing module, a bearing surface image is acquired, the image is cropped and filtered to remove noise and adjust contrast, and a processed image is obtained; the processed image is subjected to gray scale conversion to generate a preprocessing result. In the application, environmental noise interference is reduced through image cropping and filtering, the contrast is adjusted to optimize image quality, and the distinguishing degree of a defect area and a background is improved. The gray scale conversion standardizes image channel information, keeps the calculation precision of feature extraction consistent, and avoids multi-channel data interference analysis results. An image pyramid method constructs different scale image levels, in the local contrast and texture feature extraction process, the adaptability to fine cracks and large-area peeling defects is improved, and the stable recognition ability to different size defects is enhanced.