3004 results about "Contrast ratio" patented technology

The invention discloses an image enhancement method and system in a complex coal mine environment, and relates to the technical field of image processing, and the method comprises the steps: carrying out the preprocessing of a collected coal mine image of a target region, and dividing the coal mine image into different semantic regions, including a bright region, a dark region and a dust shielding region, through a deep learning semantic segmentation model; according to semantic region characteristics, a differentiation enhancement strategy is made; a traditional Retinex model is improved, non-local mean filtering is introduced, and an illumination component and a reflection component are decomposed through pixel similarity matching. According to the method, the image is divided into the bright area, the dark area and the dust shielding area through the deep learning semantic segmentation model, differential enhancement strategies are formulated according to different area characteristics, detail distortion caused by global adjustment is avoided, local contrast suppression is adopted in the bright area, illumination compensation is enhanced in the dark area, and the image quality is improved. Noise diffusion of the dust shielding area is inhibited through edge preservation smoothing, the image quality of each area is remarkably improved, and it is ensured that image details in a complex coal mine environment are clear and visible.

The invention discloses a global dynamic optimization system for an ultra-high-definition glass-based display screen. The operation process of the system specifically comprises the following steps: acquiring a real-time state data set of the display screen; generating an initial optimization parameter set based on a preset optimization strategy and the user behavior model; capturing a user interaction signal and an environment change signal in real time; generating a dynamic correction coefficient set according to the user interaction signal and the environment change signal; performing global parameter fusion based on the initial optimization parameter set and the dynamic correction coefficient set to generate a target optimization instruction set; and feeding back an optimization effect evaluation table based on the optimization effect. The method has the following advantages and effects: collaborative optimization and thermodynamic balance of global display parameters are realized, the system can balance visual experience improvement and thermal diffusion suppression requirements in real time through multi-dimensional data fusion and global parameter collaborative calculation, and the system can improve the visual experience while enhancing local contrast and optimizing color rendition. And actively inhibiting the temperature rise of the screen through thermodynamic equilibrium constraint.

The invention relates to the technical field of defect detection, and discloses an integrated circuit mask plate defect multispectral cooperative detection method, which comprises the following steps: carrying out preliminary scanning on a mask plate by using a multispectral camera, selecting an optimal wave band combination, switching to a target wave band through a liquid crystal adjustable optical filter, and obtaining a high-confidence multispectral image; the optical path offset is calculated through the linear relation, the deflection angle of the micro-mirror is dynamically adjusted, and images are collected for the second time after imaging offset is compensated; image filtering and registration are completed in the FPGA, and a high-contrast fusion image is generated; if the confidence coefficient is insufficient, triggering a wave band reselection mechanism to recheck; dynamically optimizing a defect type-spectrum mapping table based on real-time detection data; when the environment fluctuation exceeds the limit, an anti-interference mode is automatically switched, and micro-mirror calibration is triggered; and monitoring performance degradation of the optical assembly in real time, switching redundant optical paths, cleaning damaged parts, triggering manual reinspection when conflicts occur, and finally generating a visual report. According to the invention, the detection accuracy of the integrated circuit mask plate can be improved.

The invention discloses a data line surface defect rapid nondestructive detection method based on intelligent image recognition, relates to the technical field of image data processing, and aims to solve the technical problems of difficult defect feature separation and low detection accuracy under complex weaving texture noise interference, and the method comprises the following steps: S1, collecting a data line image and carrying out graying processing; s2, constructing a multi-scale image pyramid, accurately segmenting the image by adopting a self-adaptive threshold algorithm, and realizing rapid detection of surface defects of the data line in combination with Hough transform; s3, frequency domain separation of weaving texture and background noise is realized through Fourier transform, interference is filtered out in combination with a band-pass filtering technology, and then the defect contrast is enhanced through histogram equalization; according to the method, the three-layer Gaussian pyramid is constructed for multi-scale decomposition, and the band-pass filtering technology is combined, so that the weaving texture and the defect signal are effectively separated, the detection accuracy is greatly improved, and the problems of missing detection and misjudgment caused by frequency characteristic confusion are thoroughly solved.

The invention provides an underwater image enhancement method combining high-frequency enhancement, space-frequency mixed feature extraction and a Mama enhancement fusion technology. The method aims at solving the problems of low contrast ratio, color distortion, detail loss and the like caused by absorption and scattering effects in the light propagation process of the underwater image, the image quality is improved, and the visual perception effect is improved. The method specifically comprises the following four main steps: firstly, acquiring and preprocessing initial data sets of different underwater scenes; secondly, extracting texture detail information of the image through a texture enhancement module; thirdly, further enhancing texture details by using a space-frequency mixed feature extraction module; and finally, in combination with the original image, the texture information and the spatial frequency domain enhancement result, realizing feature fusion of the image through a Mama enhancement fusion module, thereby effectively recovering details of the underwater image, correcting color deviation and improving the definition and visual effect of the image. According to the method, the enhancement performance of the underwater image in a complex environment can be improved, and powerful support is provided for applications such as underwater detection, target detection, object recognition and image segmentation.

The invention discloses a high-resolution remote sensing image accurate classification system and method based on deep learning multi-modal fusion, and the method comprises the steps: S1, carrying out the preprocessing and fusion optimization of multi-modal data; S1.1, carrying out the standardization and normalization: carrying out the standardization and normalization of remote sensing data of different modals, and eliminating the influence caused by the difference between the different modals, the difference between the resolution, the spectral range and the like; for optical images, contrast may be enhanced by histogram equalization. The method aims at solving the problems of data heterogeneity, calculation efficiency, over-fitting, difficulty in labeling, real-time performance, interpretability and the like in an existing method by adopting multi-modal data optimization preprocessing, deep fusion model design, automatic labeling and semi-supervised learning, a lightweight model and hardware acceleration technology and a strategy for enhancing interpretability. Through the optimization, the system can maintain high classification precision, improve the calculation efficiency, reduce manual intervention, enhance the transparency and generalization ability of the model, and meet the actual application requirements.

The invention discloses a cut workpiece defect detection method and system based on image feature feedback, and relates to the technical field of image processing.The method comprises the steps that cut workpiece technological characteristics are obtained, a preset defect type library is constructed, a hardware system is built, and parameters are initialized; synchronously acquiring a multi-view original image, and storing and associating annotation information; de-noising the original image, enhancing the contrast, and extracting a region of interest ROI; extracting texture, shape, edge and gray features from the ROI, and screening through a Relief-F algorithm to obtain an optimal feature subset; inputting into an SVM (Support Vector Machine) model for reasoning, and screening to obtain an effective defect detection result; and calculating an evaluation index and generating a feedback signal, and performing iterative optimization after adjusting parameters. The system comprises an acquisition module, a master control module, a data processing module and a display module. Through the precise design and closed-loop feedback of the whole process, the precision, efficiency and long-term adaptability of defect detection of the complex cutting workpiece are improved, and the industrial quality management and control requirements are met.

The invention discloses an MURA defect detection method based on background reconstruction and related equipment, and relates to the field of image processing. The method comprises the following steps: acquiring an initial image displayed by a display panel; performing discrete cosine transform processing on the initial image to extract low-frequency background components; denoising the low-frequency background components based on a singular value decomposition algorithm to obtain main background features; performing fitting reconstruction on the main background features through a polynomial surface fitting algorithm to obtain a background reconstruction image; performing contrast enhancement processing on the background reconstruction image to obtain a background enhancement image; subtracting the background enhancement image from the initial image to obtain a difference image; determining an MURA defect area to be detected according to the difference image; and performing identification processing on the MURA defect to-be-detected area through a preset MURA defect detection model to obtain an MURA defect detection result of the display panel. According to the technical scheme, the MURA defect detection precision can be improved.

The invention relates to the technical field of multi-image fusion recognition, in particular to a data fusion method and system for CCD visual detection, and the method comprises the following steps: obtaining a horizontal pixel row calculation gradient construction trend sequence, repairing an edge fracture to generate an integrity index, extracting a gray value to detect feature mutation, and distributing fusion weights to establish a mapping relation. And executing image fusion and balancing the contrast to generate a fusion matrix result. According to the method, the fracture edge region is identified, interpolation compensation is executed, the structural similarity index of the local gray sequence in the image overlapping region and feature direction mutation detection are combined, accurate identification of the edge matching result is guided, and fusion weight factor mapping corresponding to signal-to-noise ratio distribution is introduced; according to the method, the distribution relation between the pixels in the region and the credible weight is effectively established, the edge transition among the multi-source images is more natural through Poisson constraint and contrast balance adjustment of the fusion region, and the structural fidelity and the judgment stability of the fusion image are remarkably enhanced.

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 discloses a liquid crystal display screen defect detection method, and relates to the technical field of liquid crystal display screen defect detection.A bidirectional polarization transmission structure light source is adopted to project 0-degree and 90-degree polarized light, interference of parasitic reflection on defect detection is reduced, and a self-adaptive exposure control unit is introduced; exposure parameters are adjusted in real time according to the light transmittance and reflection characteristics of the glass substrate, the imaging quality is optimized, and the detection effect is prevented from being affected by overexposure or underexposure; in the image processing stage, parasitic reflection information is extracted by combining a polarization difference algorithm of double-frame subtraction, high-frequency interference is removed by using fast Fourier transform (FFT), an optimized defect image after parasitic reflection suppression is obtained, real defect information is more prominent, and the defect contrast is improved; multi-scale Gaussian fitting filtering, a gradient lifting method and super-resolution reconstruction are adopted to enhance defect features in the image, so that a low-contrast defect area is easier to identify.

Provided is a low-illumination image adaptive enhancement method, an apparatus thereof, a device and a storage medium. The method includes: inputting a low-illumination image to be enhanced into a pre-trained low-illumination image adaptive enhancement model to obtain an enhanced image. A method of training the low-illumination image adaptive enhancement model includes: acquiring a training set containing the low-illumination image and the corresponding reference image; constructing the low-illumination image adaptive enhancement model based on a Retinex algorithm. The low-illumination image adaptive enhancement model includes a projection module, an illumination component module, a reflectance component module and an enhancement module. The training set is used to train the low-illumination image adaptive enhancement model, and the trained low-illumination image adaptive enhancement model is obtained. The brightness and the contrast of the image are enhanced, and at the same time, the color and structure information of the image is restored effectively.

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 provides an aluminum alloy surface oxidation spot defect identification method and device based on machine vision, and relates to the field of intelligent manufacturing and industrial automation, and the method comprises the steps: obtaining an aluminum alloy surface color image, and carrying out the preprocessing of the image, so as to extract a brightness component image; self-adaptive threshold segmentation of local contrast enhancement is carried out on the brightness component image, a defect area binary mask is generated, morphological connected domains are extracted according to the mask, and three basic feature indexes of the area pixel value, the contour Fourier descriptor complexity and the area gray scale standard deviation contrast of each connected domain are calculated; and extracting and marking a connected domain boundary, verifying a boundary closed topological structure, and dynamically generating a curvature-driven self-adaptive sampling point through multi-scale B-spline curvature extreme value detection. Through optical-algorithm-process three-level collaborative innovation, the curved surface reflection false alarm rate is reduced, the pinhole detection rate is increased, and the boundary precision is + / -0.2 pixel.

The invention discloses a sparse finite angle CBCT reconstruction method and system based on residual diffusion and a storage medium, and the method comprises the steps: carrying out the CBCT sparse finite angle scanning of a to-be-detected target, and obtaining sparse projection data; fDK reconstruction is carried out on the sparse projection data to obtain an initial CBCT image; generating a first optimized CBCT image from the initial CBCT image through an image pre-training network; through the first optimized CBCT image and the sparse projection data, using the trained residual diffusion model to determine a residual image of the to-be-detected target; summing the first optimized CBCT image of the to-be-detected target and the residual image of the to-be-detected target to obtain a second optimized CBCT image of the to-be-detected target, and the second optimized CBCT image is a final CBCT reconstruction image. According to the method, the problems of stripe artifacts and low-contrast tissue annihilation under limited angle scanning are solved, the large-view CBCT reconstruction resolution is improved, and the radiation dose is reduced.

The invention provides a self-adaptive nonlinear image enhancement method and system for a low-light scene of a mobile terminal, and relates to the technical field of image enhancement, and the method comprises the steps: carrying out the image preprocessing and noise reduction, and carrying out the graying and noise suppression of an input color image through a local variance self-adaptive algorithm; adaptive down-sampling is carried out, and the down-sampling proportion is dynamically adjusted according to the image resolution and the content complexity, so that the processing efficiency is improved; brightness adaptive enhancement is carried out, and the overall brightness of the image is rapidly improved by adopting an Otsu method and a lookup table; contrast nonlinear enhancement: enhancing image details and contrast in combination with a Laplace operator and local mean adjustment; and color restoration: restoring the resolution through bilinear interpolation and performing weighted fusion to realize natural color reconstruction. And finally, a high-quality image of which the brightness, the contrast ratio and the color are remarkably improved is output. According to the invention, the recognition accuracy and processing efficiency of the low-illumination image are improved.

The invention discloses a medical image segmentation method based on high-resolution modal guidance and cross-modal boundary perception, and the method comprises the steps: carrying out the data preprocessing and enhancement of multi-contrast magnetic resonance imaging data, obtaining a boundary mask through a Canny operator and a Dilatation operation, constructing a multi-modal low-resolution data set, and carrying out the recognition of the multi-modal low-resolution data set; meanwhile, a high-resolution T2f modal data set is reserved, and the data set is divided into a training set, a verification set and a test set; a segmentation model is constructed, and the segmentation model comprises a high-resolution mode-guided double-encoder architecture module, a cross-level attention collaboration mechanism module, and a segmentation branch and boundary prediction branch decoder module; designing a training strategy of joint optimization of boundary contour detection and region segmentation, training the segmentation model by using a training set, and storing optimal model parameters on a verification set; and carrying out model performance verification in the test set, and segmenting a to-be-tested medical image by using the verified segmentation model.

The invention relates to the technical field of machine learning, in particular to a grain classification and recognition method and system based on image analysis, and the method comprises the following steps: obtaining grain particle image data, calculating a gray histogram and a gradient change rate, calling a gray co-occurrence matrix to extract grain particle texture density, and scanning a sliding window to obtain local feature parameters of grain particles. According to the method, through combined analysis of a gray level histogram and a gradient change rate, dynamic extraction of particle textures through a sliding window, enhancement of complex surface feature capture, normalization of contrast and direction consistency parameters, construction of weighted feature vectors, dynamic correction of variance contribution degree and suppression of environmental interference, and fusion of near-infrared and short-wave infrared reflectivity changes, the near-infrared and short-wave infrared reflectance change is improved. And quantifying the mean value and peak-to-valley ratio of spectrum difference values, matching visible light characteristics to verify multi-dimensional constraints, constructing a spectrum-texture matrix by local reflectivity rate and gradient change rate, and synchronously evaluating global similarity and spatial distribution difference by adopting Euclidean distance and local offset double-layer matching.

The invention relates to the field of liquid crystal display, and discloses a liquid crystal display screen dynamic contrast optimization method and system, and the method comprises the following steps: collecting ambient light intensity and backlight module temperature parameters in real time, and combining with image content entropy analysis to dynamically generate a non-uniform backlight partition grid; a brightness diffusion physical model is constructed based on hardware parameters such as the refractive index of the light guide plate and the LED spacing, and the optimal brightness of each partition is solved by adopting a temperature self-adaptive improved particle swarm algorithm; driving voltage is generated through piecewise nonlinear gamma mapping and inverse function iterative calculation, and the driving voltage is adapted to the nonlinear response characteristics of the backlight module; a hardware accelerator is used for executing a diffusion equation and optimization calculation in parallel, and model parameters are dynamically adjusted in combination with feedback of an optical sensor, so that a closed-loop control link is formed. According to the method, the contrast performance and the dynamic scene adaptability of the display screen are comprehensively improved through dynamic partition adaptive to the environment, an optimization algorithm driven by a physical model and real-time processing accelerated by hardware.

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 belongs to the technical field of computer vision, and discloses a wire harness crimping defect detection system and method based on machine vision, and the system comprises a multispectral light source control module which is used for employing an annular LED light source and coaxial light mixed illumination and adapting to different wire harness surface materials by dynamically adjusting the angle and intensity of the light source; the multi-dimensional perception acquisition module is used for acquiring wire harness image data and crimping geometric data in real time based on the dynamically adjusted light source angle and intensity; the visual enhancement fusion module is used for de-noising the wire harness image data by using a non-local mean de-noising algorithm and dynamically adjusting and controlling a similarity measurement range through a smooth factor; superposing a multi-scale Retinex algorithm, and enhancing the texture contrast of the wire harness image data through the local contrast; the wire harness image data and the crimping geometric data are fused, and a crimping part multi-mode characteristic graph is constructed; the accuracy of defect detection is facilitated, and potential problems can be found and solved in time.

The invention provides an infrared and visible light image perception enhancement fusion method and system based on deep learning, and the method employs a multi-stage training strategy, and comprises the steps: carrying out the single-mode reconstruction training of an encoder and a decoder, so as to guarantee the feature extraction and reconstruction capability; based on a pre-training encoder and a pre-training decoder, performing multi-scale decomposition by applying discrete wavelet transform, and performing enhancement and fusion on the basic features and the detail features respectively; and designing an illumination perception module and an illumination consistency loss function to optimize the brightness and contrast of the fused image. The method effectively improves detail retention, brightness uniformity and color expression of the image under the low-light condition, and is particularly suitable for application scenes such as night high-perception requirements and the like.

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.

According to the Mama and diffusion model-based OCTA fundus image generation system, method, medium and device provided by the invention, the unique Mama block is designed to effectively predict the accurate diffusion noise of the OCTA image from the OCT potential noise, the artifact of the generated image is reduced, and the contrast of the generated OCTA image is improved. Secondly, the proposed bidirectional Bow scanning scheme effectively alleviates the mechanism challenge of original Mama scanning, enhances the spatial continuity of the generated OCTA image, and improves the vascular extension of the generated image. Besides, the understanding ability of the OCT image is improved by combining a Mama block and a diffusion model through designed condition prompt, and the authenticity of the generated fundus OCTA image is further improved. According to experimental verification on two public data sets of OCTA500-3M and OCTA500-6M and one private data set, the SSIMs of 87.5% and 88.1% are achieved respectively, it is indicated that the fundus OCT image information can be fully utilized, the high-quality fundus OCTA image can be effectively generated, and new possibility is provided for low-cost screening of retinal diseases.

The invention relates to the technical field of display, and particularly discloses a dynamic ambient light adaptive OLED display optimization method and system, and the method comprises the steps: obtaining an ambient light time sequence data set of an environment where an OLED display screen is located, and synchronously obtaining real-time display parameters and historical brightness adjustment data of the display screen; acquiring ambient light sensing features according to the ambient light time sequence data set; obtaining photo-thermal structure coverage area data of the OLED display screen according to the real-time display parameters; and acquiring preliminary optimization parameters according to the ambient light sensing characteristics and the photo-thermal structure coverage area data. According to the invention, illumination intensity, spectrum drift and contrast change are captured in real time in combination with ambient light perception characteristics, the one-sidedness of adjustment only according to brightness traditionally is solved, reflection can be reduced through photothermal structure deformation under strong light, brightness can be improved to offset interference, and human visual characteristics can be matched under weak light. And the display definition and the visual comfort in different environments are effectively improved.

The invention relates to the technical field of image enhancement, in particular to a protector bimetallic strip detection method based on machine vision, and the method comprises the steps: obtaining a surface image of a bimetallic strip, and carrying out the preprocessing of the surface image, and obtaining a gray image; performing threshold segmentation on the grayscale image to obtain at least one segmentation region; obtaining an evaluation coefficient of each segmented region, obtaining an adaptive cutting parameter when contrast-limited adaptive histogram equalization is carried out on each segmented region according to the evaluation coefficient of each segmented region, and carrying out image enhancement on each segmented region in the grayscale image according to the adaptive cutting parameter to obtain an enhanced grayscale image; the corrosion detection result in the bimetallic strip is obtained by using the enhanced gray level image, so that the enhanced image can reflect more effective information, and the surface corrosion detection precision of the bimetallic strip according to the enhanced image is improved.

The invention discloses a phase residual error-based shallow defect detection method and device and a storage medium, which are used for improving the detection precision of shallow defects. Obtaining a reflection fringe pattern of the display screen to be detected; performing phase recovery on the reflection fringe pattern to generate first phase recovery data; calculating and generating a first phase gradient amplitude diagram according to the first phase recovery data; constructing a local average background gradient at each pixel according to the first phase gradient magnitude image; generating a first gradient residual image according to the first local average background gradient image and the first phase gradient magnitude image; performing visual contrast enhancement processing on the first gradient residual image; self-adaptive threshold judgment is carried out on the first anomaly enhancement graph, and a first structure defect mask graph is generated; performing feature fusion on the first phase gradient amplitude image, the first anomaly enhancement image and the first structure defect mask image; and inputting the first feature fusion image into a target shallow layer defect identification model for shallow layer defect detection, and generating a shallow layer defect detection result.

The invention discloses an image enhancement method and system based on deep learning, and the method comprises the following steps: obtaining an image pixel matrix, extracting multi-region features according to a brightness difference value, a gradient direction and a texture density partition frequency, carrying out the weighting of the multi-region features, generating a response graph, carrying out the scale decomposition, extracting multi-scale features, fusing a residual image, and enhancing the detail inhibition redundancy. And calling the multi-image parameter to callback the brightness, and outputting an enhanced image. According to the method, accurate separation of high and low frequency regions is realized through region frequency attribute division, fuzzy and excessive enhancement caused by unified processing are avoided, a differentiation strategy is adopted for different regions to improve high-frequency details and suppress low-frequency redundancy, and feature response weight maps are subjected to weighted stacking and normalized enhancement of local textures and contrast. Scale decomposition is combined with multi-scale fusion to improve naturalness and avoid visual discomfort, and edge consistency matching optimizes brightness callback to enhance edge definition and overall visual performance.

The invention belongs to the field of three-dimensional measurement, and discloses a three-dimensional reconstruction method based on structured light image enhancement, which comprises the following steps of: generating a stripe coding pattern: generating four sine stripe patterns with different phase shift amounts by using MATLAB programming, and projecting the sine stripe patterns to the surface of an object to be measured; calibrating a camera; calibrating a projector; the method comprises the following steps: capturing four phase shift stripe images projected on the surface of an object by using a camera, and preprocessing the stripe images collected by the camera through a GF-CL image enhancement algorithm; acquiring a wrapped phase: extracting wrapped phase distribution through a four-step phase calculation formula; generating a Gray code; carrying out binarization conversion; unwrapping the phase by using a complementary Gray code; and performing three-dimensional reconstruction. According to the invention, the contrast is improved by using an image enhancement algorithm, and noise is effectively removed. In addition, three-dimensional reconstruction is carried out by combining a four-step phase shift method and a complementary Gray code, so that the error of edge hopping in the traditional Gray code reconstruction is eliminated, and no hopping and interruption are ensured when a real phase is obtained.