1642 results about "Contrast ratio" patented technology

The invention relates to the technical field of image processing, and discloses an industrial part alignment method and system based on visual analysis and a storage medium. The method comprises the steps that a three-view camera collects an industrial part image, and preprocessing is carried out through gradient magnitude local contrast enhancement to obtain an enhanced image; performing hierarchical feature extraction to identify edge contours and key control points to form a multi-dimensional feature set; and establishing a dynamic reference coordinate system based on the feature set to obtain a part space attitude matrix. And the attitude deviation is compensated through Z-axis offset and rotation coupling error analysis. Posture adjustment is decomposed into a plurality of sub-stages, an alignment track is optimized by adopting a variable speed planning strategy, and accurate alignment of the parts is achieved. The problems that multi-view visual information fusion is insufficient, a special recognition algorithm for geometrical characteristics of the industrial parts is lacked, and Z-axis offset and rotation coupling error compensation is inaccurate in the posture adjustment process are solved, and the precision and stability of alignment of the industrial parts are improved.

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 the technical field of special equipment nondestructive testing image intelligent identification, and discloses a special equipment nondestructive testing image defect automatic identification method and system, and the method comprises the steps: carrying out the preprocessing based on an anisotropic diffusion mechanism; a local self-adaptive threshold method is adopted to complete preliminary defect area positioning; constructing a scale consistency constrained multi-scale image pyramid; constructing defect morphological parameters; and carrying out defect type identification by fusing fuzzy form constraint and an SVM classification mechanism. In the prior art, a global filtering or fixed threshold segmentation method is mostly dependent, and especially under the conditions of V-shaped grooves, multi-layer weld structures and corrosion perforation defects, accurate identification of irregular, multi-scale and weak-contrast defects cannot be realized. According to the method, the anisotropic diffusion mechanism is introduced, the multi-scale response extraction of scale consistency constraint is combined, and the classification strategy of fuzzy form modeling is fused, so that the accuracy of special equipment image defect identification is 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 provides a wafer defect detection method and system and electronic equipment, and relates to the technical field of machine vision, and the method fully utilizes the transformation relation between an imaging unit and an objective table in the wafer defect detection process to precisely splice strip images, thereby obtaining a high-precision wafer image, and improving the wafer detection precision. Meanwhile, an improved VAE model can be adopted to accurately obtain the defect area of the wafer to be detected from the brightness, the contrast ratio and the structural difference, high-precision recognition can be carried out on the fine defects, and therefore the problem that in the prior art, the fine defect detection effect is poor is solved.

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 discloses a control method of a video monitoring system, which relates to the technical field of video monitoring, and comprises the following steps of: acquiring continuous image frames through the video monitoring system, extracting brightness channels, edge structures, direction gradients and contrast changes of images, constructing a reflective perception vector group, and calculating an included angle change trend by combining a target motion direction vector, determining whether a reflection offset condition consistent with the target direction appears in a picture in a camera attitude control process; after it is determined that the reflection offset condition consistent with the target direction appears in the picture, the inter-frame change tensor of the target area and the reflection area is extracted, and a time sequence track consistency matrix is constructed; according to the invention, the problem of wrong adjustment of the camera caused by misjudgment of light reflection in video monitoring is solved, attitude regulation and control based on image displacement abnormal mode recognition are realized, and the tracking stability and the monitoring accuracy are improved.

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 belongs to the technical field of image enhancement, and particularly relates to an image enhancement method for VCSEL (Vertical Cavity Surface Emitting Laser) epitaxial wafer detection, which comprises the following steps of: analyzing local and global gray information and structure tensor characteristics of pixel points, and calculating a defect saliency value of each pixel point to quantify the possibility of the pixel point as a defect; interference of a stripe structure in an epitaxial wafer image is weakened; a weighted histogram is constructed based on the defect saliency value, so that the defect pixel occupies a higher weight in the histogram; and determining an optimal segmentation threshold value by searching an energy median point of the weighted histogram, and finally performing BBHE enhancement on the image by using the segmentation threshold value. According to the method, the contrast ratio of the tiny defects can be remarkably improved, a high-quality image is provided for subsequent epitaxial wafer detection, and the detection accuracy is 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 an underwater image enhancement method fusing multi-scale frequency domain and multi-color gamut features, and the method comprises the following steps: employing an underwater robot to collect an original image, carrying out the size normalization processing, and obtaining a standardized image; inputting the standardized image into a frequency domain feature enhancement network to generate an enhanced frequency domain feature; forming the standardized image into three paths of color features; splicing and fusing the three paths of color features to obtain enhanced color features; splicing and fusing the enhanced color representation and the frequency domain representation to obtain a final joint feature; and L1 loss and structural similarity loss are adopted for joint supervision, and network training is completed. According to the method, multi-scale frequency domain enhancement and multi-color gamut information enhancement are coupled in the same frame, so that collaborative repair of multiple degeneration such as color distortion, contrast reduction and detail blur of the underwater image is realized.

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 overexposure and underexposure image enhancement method, device, equipment and medium, and the method comprises the steps: enabling an image decomposition network to respectively receive a first illumination image and a second illumination image through employing two encoder-decoder networks sharing the weight, and extracting multi-scale illumination distribution information through multi-scale connection; the image reconstruction module outputs a corresponding reflectivity component and a brightness component, the image enhancement network adopts an image enhancement sub-network to adjust illumination distribution and suppress noise based on the reflectivity component and the brightness component, and the image reconstruction module multiplies the adjusted reflectivity component with an illumination image element by element and outputs an enhanced image. According to the method, the technical effects of effectively recovering image details, reducing noise interference and improving real-time performance under the overexposure or underexposure condition are reflected, the method is particularly suitable for complex environments such as power equipment monitoring, the contrast ratio of the enhanced image is better, artifacts are fewer, the calculation burden is reduced, and efficient batch processing prediction is supported.

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 image processing, and particularly discloses a low-light environment image adaptive enhancement method based on unmanned aerial vehicle inspection, which comprises the following steps: preprocessing a low-light RGB image shot by an unmanned aerial vehicle; the preprocessed low-light RGB image is converted to an HSV color space, and brightness and color components are obtained through separation; performing dual-channel attention mechanism processing: respectively optimizing brightness and color components by adopting a brightness branch and a color branch; wherein the brightness branch enhances the dark part contrast through a convolutional network, and the color branch suppresses a color channel with significant noise based on a channel attention mechanism; and reconstructing the optimized color and brightness components into an HSV color space, and converting the HSV color space into an RGB image format to realize brightness enhancement and color denoising. The problems of overexposure, color distortion, excessive noisy points and the like existing in the image shot by the unmanned aerial vehicle camera in the low-light environment in the prior art can be effectively solved, and the image quality under the night or weak light condition is improved.

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 an asphalt mixture component segmentation method, device and equipment, and relates to the technical field of asphalt pavement construction control, and the method comprises the steps: obtaining an initial two-dimensional image of an asphalt mixture section, and carrying out the preprocessing of the initial two-dimensional scanning image, and the preprocessing comprises the contrast enhancement and light field correction. Component division is carried out on the preprocessed image through a double-threshold segmentation method, morphological post-processing is carried out on the initial segmentation image, and each separated component segmentation image is obtained; and calculating the difference value between the image area ratio and the actual volume ratio of each component segmentation image, and calculating the relative error between the image screening grading and the actual screening grading of the aggregate components. When the difference value or the relative error exceeds the preset threshold range, the image processing parameters are adjusted, the segmentation operation is executed again, the target segmented image is obtained, the difference value and the relative error of the target segmented image are both within the preset threshold range, and the problem that the segmentation precision of the asphalt mixture components is insufficient is solved.

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.