2431 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 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 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 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 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 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 provides a hull surface defect detection system based on machine vision, and relates to the technical field of data processing. The image correction module is used for carrying out illumination equalization processing and geometric distortion correction; the region construction module is used for identifying a defect-free stable region and generating reference region data which comprises a brightness model and a texture model; the candidate generation module is used for detecting a region where texture interruption or abnormal bright spots exist locally to form candidate defect data, and the candidate defect data comprise pixel positions and local contrast parameters; the stability judgment module is used for carrying out projection matching in the multiple frames of images and simultaneously carrying out joint comparison with the brightness model and the texture model of the reference area data to form real defect data and false defect data; the result output module is used for generating a detection result containing defect coordinates, defect contours, image frame numbers and interference sample prompts; the accuracy of hull surface defect detection is improved.

The invention discloses a tunnel detection method based on a color image, and relates to the technical field of tunnel detection, and the method comprises the following steps: obtaining color image data in a tunnel, and carrying out the illumination condition calibration of the image data, so as to recognize the illumination change caused by natural light, an artificial light source and a light source fault. According to the method, through illumination calibration, adaptive color correction and dynamic contrast enhancement technologies, the problems of image color distortion and detail loss under the complex illumination condition of the tunnel are solved, accurate extraction of color features and clear presentation of details of each region are ensured, and the robustness and precision of defect detection are remarkably improved. In combination with edge detection, color feature extraction, shape analysis and a machine learning algorithm, automatic application of a multi-modal identification technology is realized, misjudgment areas are automatically filtered, a defect detection report with high confidence is generated, and the defect detection report comprises crack length, leakage area and deformation classification. Accurate and comprehensive data support is provided for tunnel safety assessment and maintenance decision.

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 multi-modal fusion unmanned aerial vehicle remote sensing image target detection method and system, aims to improve the target detection precision under a low illumination condition and reduce the calculation overhead, and is particularly suitable for unmanned aerial vehicle remote sensing image processing. The method comprises the following main steps: S1, carrying out denoising, standardization and size adjustment on an input remote sensing image, ensuring image quality and consistency, and stabilizing subsequent processing steps; and S2, based on a Retinex principle, enhancing the low-illumination image through a double-branch network, and improving details and contrast of the image. And S3, carrying out feature interaction fusion on the enhanced RGB image and the infrared image by adopting a Transform model based on a self-attention mechanism, capturing complementary information of different modes, and generating fusion features for target detection. And S4, target regression and classification are carried out by adopting a multi-scale detection head based on FPN and PAN structures, and the detection precision of targets of different sizes is enhanced. Through multi-modal feature fusion, adaptive anchor frame generation and multi-scale detection, the target detection precision in low-illumination and complex environments is effectively improved, the calculation overhead is low, and the method is suitable for target detection tasks of real-time unmanned aerial vehicle remote sensing images. Experimental results show that the method is excellent in performance on VisDrone and LLVIP data sets, and the target detection precision is remarkably improved especially under the low illumination condition.

The invention relates to the technical field of optic nerve detection, in particular to a plastic particle quality detection method and system based on optic nerves, and the method comprises the following steps: capturing a plastic particle image through an industrial camera, carrying out the balance processing of the image contrast, extracting the brightness and color features of the image, and removing the noise interference; and adjusting the image brightness and optimizing the particle edge to obtain a particle optimization image. According to the invention, the accuracy and automation level of plastic particle quality detection are greatly enhanced by using the visual neural network and the image processing technology, and the edge and shape features of the particles can be accurately extracted from a complex background by automatically adjusting the image contrast and brightness and applying an advanced edge detection algorithm. According to the method, the visual quality of the image is optimized, tiny flaws such as cracks and bubbles of the particles can be effectively recognized and analyzed, and more detailed data support can be provided compared with a traditional method by accurately calculating the surface roughness and texture uniformity of the particles.

The invention provides an improved multi-frame image target detection network, namely, TFA-Net (Temporal Fusion Attention Network), which is oriented to a welding seam defect detection task. According to the network, continuous multiple frames of images are used as input, and spatial features of each frame of image are extracted through a ResNet backbone network sharing parameters. On the basis, a multi-stage feature fusion module is fused and introduced, effective integration among features of different scales is realized, and the perception capability for small-size and weak-contrast defects is improved. In order to further capture dynamic information of the target in the time dimension, a time sequence modeling module is designed, modeling is carried out on a multi-frame feature sequence, and continuous features of the target changing along with time are extracted. And then, the network adopts a gating fusion mechanism to carry out adaptive weighted fusion on the static space features and the dynamic time sequence features, and the robustness and the discrimination capability of feature representation are enhanced. Finally, the fusion features are input into a decoder module, the category probability of the defects is predicted through a classification sub-network, anchor frame position offset is calculated through a frame regression sub-network, and accurate positioning and recognition of the multiple types of defects in the weld seam image are achieved. The network has the advantages of clear structure, strong generalization ability, high adaptability and the like, and is especially suitable for the problems of small target size, unclear texture, strong motion continuity and the like in a welding seam detection scene.

The invention relates to the technical field of display, and particularly discloses an intelligent self-adaptive eye protection display system which sequentially comprises a content analysis module, an ambient light detection module, an eye protection parameter calculation module, a display parameter adjustment module and a user feedback module. The system identifies the screen content type and the dynamic degree in real time, synchronously detects the ambient light intensity and the color temperature, obtains the optimal combination of the blue light proportion, the brightness, the contrast ratio, the color temperature and the sharpness through a multi-target optimization model, and smoothly adjusts the display through driving; a user can score, finely adjust and mark a scene, and feedback data enters a self-learning engine to iteratively update a parameter weight so as to form a content-environment-user closed loop. The system can significantly reduce blue light harm and improve visual comfort without external hardware.

The invention discloses a production quality evaluation method for a gold bonding wire, and relates to the technical field of microelectronic packaging, and the method comprises the following steps: obtaining a surface image of the gold bonding wire through a high-resolution image collection system; performing preprocessing on the image data based on the acquired image to eliminate noise and enhance contrast; through the image data, extracting surface topography gradient features by using morphological operation, and quantifying microstructure changes of the surface of the gold wire; extracting texture energy characteristics through wavelet multi-scale decomposition, and analyzing distribution characteristics of surface textures from different scales; self-adaptive fusion is carried out on the morphological gradient features and the texture energy features, and the feature expression of the defect area is enhanced by dynamically adjusting the weight; dividing the boundary of a defect region by adopting an adaptive threshold segmentation method, and separating a normal region from an abnormal region; and extracting geometric and textural features of the defect region obtained by segmentation, realizing automatic discrimination of defect types through a classification model, and outputting a quality evaluation result.

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 relates to the technical field of image data processing, in particular to a method for detecting the surface quality of a mining anchor cable steel strand after stabilization treatment, and the method comprises the steps: obtaining a surface image of a to-be-detected steel strand; determining a local texture collaboration degree index of each pixel point; determining a longitudinal consistency deviation index of each pixel point; determining a defect significance index of each pixel point; and identifying a defect area in the surface image of the to-be-detected steel strand based on the defect saliency index of each pixel point. According to the method, a local texture synergy degree and longitudinal consistency deviation index is constructed, and the gray change characteristics and the spatial change rate are combined, so that multi-dimensional quantitative detection of small defects on the surface of the steel strand is realized, noise and real defects are effectively distinguished, the sensitivity to low-contrast defects is improved, missing detection and false alarm are reduced, and the detection accuracy is improved. And a high-reliability quality detection method is provided for the mining anchor cable steel strand.

The invention relates to the technical field of optical elements, and discloses a fog display point diffusion optical system and a preparation method thereof, and the preparation method comprises the steps: obtaining user visual parameters, and building a visual behavior probability density model through kernel density estimation; designing the front surface of the lens based on the model, and generating an asymmetric defocus system coupled with a gazing habit; the rear surface of the lens is designed, and the distribution density of a fog display point diffusion unit is cooperatively modulated by the front surface defocusing amount, the fixation probability and the pupil diameter; and finally integrating front and rear surface design to form an optical lens body. According to the method, the asymmetric defocus field on the front surface and the fog display point diffusion field on the rear surface are subjected to collaborative design, and the defocus intensity and the contrast modulation intensity are accurately applied to the effective retina area of the user through the visual behavior probability density model; the technical problem that a traditional out-of-focus lens is fixed in signal and cannot adapt to individual staring habits and physiological parameter changes is solved.