834 results about "Contrast enhancement" patented technology

The invention discloses a quartzite surface defect detection method, system and device, and relates to the technical field of image processing, and the method comprises the following steps: fixing a to-be-detected quartzite on a detection platform, and carrying out preprocessing; constructing a double-path polarization imaging light path; polarization parameter optimization is carried out based on the optical anisotropy characteristic of quartz stone, so that a polarization state difference is generated between a defect area and a normal area; under the condition of polarization parameter optimization, synchronously acquiring a first polarization image and a second polarization image through a double-path polarization imaging light path; performing polarization difference calculation on the first polarization image and the second polarization image to generate a polarization difference image; and carrying out contrast enhancement processing on the polarization difference image, identifying a defect area, carrying out defect classification, and outputting a quartz stone surface defect detection result. By optimizing polarization imaging parameter configuration and combining image processing, high-precision detection and intelligent classification of quartz stone surface defects are achieved, and the automation level and reliability of detection are improved.

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 building information modeling, in particular to a building drawing geometric feature extraction and visual modeling method and system, and the method comprises the steps: carrying out the self-adaptive noise reduction, contrast enhancement and line refinement processing of an original building drawing image, and carrying out the automatic layer separation based on colors and line types; linear geometric features and specific symbol geometric features in the drawing image are extracted, and a geometric feature set is constructed; component instantiation, attribute assignment and topological relation reasoning are carried out by using a predefined building component semantic rule base, and a building component semantic network is generated; and mapping the semantic network to a parameterized three-dimensional modeling engine, calling an IFC standard three-dimensional template, performing parameter driving and automatic assembly, generating a three-dimensional building model with semantic information and a spatial structure, and performing visual output. According to the method, efficient and standardized conversion from a two-dimensional building drawing to a three-dimensional building model can be realized, and the method has the remarkable advantages of processing complex drawings and high-precision modeling.

The invention discloses a multi-label electrocardiogram classification method based on self-supervised pre-training and multi-modal semantic alignment, which belongs to the technical field of artificial intelligence, and comprises the following steps: realizing self-supervised pre-training of unlabeled data through a single-modal contrast enhancement network, generating global and local contrast views by adopting a multi-scale random cutting strategy, and classifying the global and local contrast views in a multi-scale random cutting mode; in combination with a teacher-student network architecture, the potential invariance features of the ECG signals are learned while negative sample dependence is avoided, the problem of annotation data scarcity is effectively relieved, and the feature robustness is improved. A multi-modal fusion mechanism based on label semantic guidance is provided, a time domain signal and a frequency domain time-frequency graph are mapped to a unified semantic space through fine-grained semantic alignment, local feature enhancement and cross-modal complementary information fusion are realized by using a cross attention mechanism, and the problem of semantic difference caused by modal heterogeneity in a traditional method is overcome. A multi-label comparison loss function based on a disease co-occurrence relation is proposed, a category discrimination boundary is dynamically optimized by modeling a label co-occurrence probability, the feature separability of a tail category is improved while the head category discrimination ability is enhanced, and the problem of sample category imbalance in a multi-label scene is remarkably relieved.

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 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 discloses a black pig image segmentation method based on multi-feature fusion, and relates to the technical field of image processing, and the method comprises the steps: obtaining a black pig image, and carrying out the contrast enhancement processing; inputting the enhanced image into a residual convolutional network to generate a depth feature map, extracting a local shape feature map through curvature threshold segmentation and a double fitting strategy, and fusing the two feature maps to generate a black pig feature map; establishing a spatial position prior probability graph based on the black pig sample library, calculating regional correlation and performing adaptive weighting to obtain a fusion feature graph; boundary segmentation and iterative optimization are carried out on the fused feature map based on the dynamic behavior pattern map and the attitude constraint rule, and an initial segmentation map is generated; and adopting a group behavior model as an optimization criterion, correcting the boundary of the initial segmentation image, and outputting a final segmentation result. According to the method, the segmented enhancement function based on the double-peak characteristic and the local texture feature self-adaptive adjustment strategy are constructed, so that differential enhancement of image preprocessing is realized.

The invention provides a digital information processing method for a hospital radiology department, which comprises the following steps: S1, multi-modal image collaborative acquisition and standardization: synchronously acquiring anatomical structure images, functional images and metabolic parameter data of a patient through radiology department imaging equipment, and converting the anatomical structure images, the functional images and the metabolic parameter data into space-time aligned three-dimensional digital matrixes; s2, image quality optimization processing: performing nonlinear contrast enhancement and noise suppression on the original image to improve the signal-to-noise ratio of a target area; s3, dynamic self-adaptive registration: according to the biomechanical characteristics of the organ, fusing the rigid transformation model and the elastic deformation model, and according to the digital information processing method for the hospital radiology department, based on the dynamic registration matrix of the biomechanical model, improving the multi-modal image fusion precision; a deep learning segmentation algorithm fused with morphological constraints improves the focus boundary recognition accuracy; a texture mapping three-dimensional reconstruction technology is mixed, and an anatomical structure and metabolism information are presented at the same time; the invention discloses a structured report automatic generation system based on an attention mechanism.

The invention relates to the technical field of substation equipment fault diagnosis, in particular to a substation equipment thermal fault diagnosis method and system based on an infrared image. The invention discloses a substation equipment thermal fault diagnosis method based on an infrared image. The method comprises the following steps: acquiring an equipment temperature distribution image through an infrared thermal imager; carrying out denoising, contrast enhancement and normalization preprocessing on the image; extracting features such as temperature anomaly, temperature gradient and hot spot areas; a YOLOv13 model is adopted to identify the equipment type; inputting the features into a deep learning model for fault classification; and implementing multi-level alarm according to the classification result confidence. According to the method, temperature gradient analysis and a regional dynamic contrast enhancement technology are creatively fused, so that the fault detection precision and the early warning capability are remarkably improved, and intelligent diagnosis and graded early warning of the thermal fault of the substation equipment are realized.

The invention provides an infrared thermal imaging image processing method for a pain area, and relates to the field of image enhancement, and the method combines multi-scale adaptive contrast enhancement and a bidirectional circulation mechanism, captures global and local temperature features of an infrared image, constructs an iterative feature enhancement module through an incremental fusion strategy, and carries out the image enhancement through the iterative feature enhancement module. Different level features are fused in stages, learnable parameters are introduced to dynamically adjust the new and old feature fusion proportion, the adaptability and flexibility of the model are improved, and a staged training strategy is adopted, so that the model can learn low-level thermal image information and also can extract high-level structures and thermal anomaly semantic features. According to the method, the adaptability of the model to different types of focus areas is enhanced, and the actual demand of efficient and automatic processing of the infrared image is met.

The invention discloses a liquid medicine foreign matter detection method and system based on machine vision, and the method comprises the steps: obtaining multiband spectral data of a liquid medicine sample, and carrying out the normalization processing to generate a liquid medicine background standard spectral feature template; comparing the refractive index spectral characteristics of the template and the foreign matter-containing liquid medicine, and recording the surface roughness scattering mode and polarized light reflection angle change data of a foreign matter area; calculating a spectrum peak displacement quantized value according to the polarized light data, and extracting foreign matter edge spectrum gradient information in combination with a threshold value; calculating a transparency spectral attenuation coefficient based on the edge gradient information, and obtaining spectral contrast enhancement factors of different foreign matters in combination with the spectral response characteristic data; and if the enhancement factor does not reach the standard, adjusting the spectrum correction parameter and recalculating, fusing the scattering mode and the multi-angle incident spectrum response result to carry out consistency verification, and obtaining a final identification result containing the foreign matter material type and the danger level. The method can improve the drug detection accuracy and guarantee the drug quality safety.

The invention relates to the technical field of image analysis, in particular to a mobile phone screen uniformity detection method based on image analysis, which comprises the following steps: extracting a central region gray sequence, calculating a slope to generate a trend chart, positioning a change starting point to adjust coordinates, extracting gray difference to enhance comparison, and analyzing a bright-dark span to generate a map. And extracting a gravity center gray level judgment difference and outputting a detection scheme. According to the method, through multi-direction gray scale sequence slope extraction and fluctuation aggregation feature construction, detail capture of a brightness trend is enhanced, reverse gray scale change starting point positioning and coordinate offset are combined, abnormal region positioning precision is improved, a local transition zone and a jump section are extracted, and gray scale fluctuation partition enhancement is realized. Through high-gray pixel set gravity center and local fluctuation difference analysis, brightness abnormal points are accurately recognized, the whole process is fused with the links of gray trend extraction, change track adjustment, contrast enhancement, gravity center analysis and the like, the detection sensitivity and the judgment precision are enhanced, and the screen brightness differential detection requirement is met.

The invention relates to a small-sample contrast enhancement fine tuning method and system based on a large language model, which are used for identifying named entities in recruitment texts. The method comprises the steps of performing cleaning and format conversion on an original recruitment text, and generating an input sample conforming to a natural language instruction format; under the condition that the labeled samples are insufficient, positive and negative sample pairs are constructed to enhance the recognition capability of the model on entity categories and boundaries; carrying out low-rank parameter updating on the pre-trained large language model by adopting a LoRA fine tuning technology, and reducing computing resource consumption in combination with 4-bit quantitative training; in a pre-training large language model reasoning process, through a multi-dimensional joint confidence evaluation mechanism, confidence of four dimensions of entity levels, lengths, types and contexts is synthesized, and low-confidence identification results are filtered after dynamic weighted normalization processing. The method is suitable for recruitment recommendation, talent matching and other downstream tasks, and has the advantages of high recognition accuracy, low training cost, high system robustness and the like.

The invention relates to the technical field of image processing, and discloses a PDC (Polycrystalline Diamond Compact) mold quality detection method. The method comprises the following steps: scanning a PDC composite sheet mold through a multi-frequency ultrasonic probe to obtain a multi-channel ultrasonic image, and performing denoising and contrast enhancement by using a deep convolutional neural network. Features are extracted through an improved MesoNet algorithm, an enhanced image is generated through a weighted fusion strategy, and finally quality grade classification is conducted through image segmentation and defect area calculation. According to the method, the technical problems of difficulty in multi-frequency ultrasonic data fusion, inaccurate defect feature extraction, insufficient image segmentation precision and low quality grading standardization degree in the quality detection of the PDC composite sheet mold are solved. Through cooperative scanning of the multi-frequency ultrasonic probe, improved MesoNet algorithm feature extraction, U-Net image segmentation and an intelligent quality classification technology, the accuracy and the automation level of PDC composite sheet mold defect detection are improved.

The invention provides a dark image preprocessing method and device based on machine vision, equipment and a medium, and relates to the technical field of agricultural automation, and the method comprises the steps: constructing a multi-scale Retinex enhanced fusion adaptive gamma correction illumination compensation model, carrying out the processing of an original garlic seed screening image through the illumination compensation model, eliminating illumination unevenness and light reflection interference, and obtaining an original garlic seed screening image; obtaining a first garlic seed screening image; according to a texture complexity detection method based on a gray-scale guide map, designing an adaptive joint bilateral filter, and processing the first garlic seed screening image through the adaptive joint bilateral filter to obtain a second garlic seed screening image; and performing multi-resolution contrast enhancement processing on the second garlic seed screening image through a pyramid decomposition and entropy-driven partitioning strategy to obtain a preprocessed garlic seed screening image. According to the method, the defect detection accuracy and the classification efficiency can be effectively improved under the conditions of high noise and complex backgrounds, and robust visual preprocessing support is provided for subsequent automatic sorting.

The invention discloses a multi-modal dynamic compensation road disease intelligent detection and risk assessment system, and relates to the technical field of artificial intelligence and computer vision, and the system comprises an image collection module which is used for obtaining a road surface image in real time through a camera device, and transmitting the image to a preprocessing module; the preprocessing module is electrically connected with the image acquisition module and is used for carrying out graying, noise reduction, contrast enhancement and geometric correction operation on the image and outputting a standardized image; the feature extraction module is electrically connected with the preprocessing module. According to the road disease detection system provided by the invention, by integrating a plurality of modules, high efficiency and intelligence of road disease detection are realized, compared with traditional manual inspection, the system not only improves the detection efficiency, but also remarkably enhances the objectivity and accuracy of detection, and is particularly suitable for real-time monitoring requirements of a large-scale road network; the image acquisition quality is effectively improved, and the effectiveness of feature extraction can be ensured.

The invention provides a CLAHE image enhancement optimization method and system based on an FPGA and a medium. The method comprises the steps that firstly, an input image is divided into a plurality of histogram sub-regions; on the basis of histogram statistics of each sub-region, the histograms of the sub-regions are cut, pixels exceeding a threshold value are redistributed according to a given algorithm, and the histograms are corrected; mapping is carried out through CDF operation to obtain an equalized gray level, and then gray stretching is carried out to generate a new gray mapping result; and finally, reading the mapping gray level stored in the previous frame, processing the gray value of the pixel point through combined operation of bilinear interpolation, completing block effect elimination between the sub-regions, and outputting an image after local contrast enhancement. According to the method provided by the invention, the controllability of the overall brightness level of the image can be ensured on the basis of ensuring the enhancement effect of the video image. And meanwhile, the method is realized on an FPGA platform, so that the processing speed of image enhancement is ensured, and the dual requirements on the processing speed and quality can be met.

The invention discloses a motion blur removing method based on dynamic image interpolation, and the method comprises the steps: firstly correcting a blur track according to the dynamic change of time and direction; then, considering the stage speed change of the target, and carrying out weight updating on the initialized fuzzy kernel; then, under the condition that the target speed cannot be estimated, feature points are detected by using an SIFT algorithm, and the feature points are tracked by using an L-K optical flow method, so that a more accurate dynamic fuzzy kernel is constructed, and then deconvolution operation is performed by combining a neighbor interpolation technology, so that the definition of an original image is recovered; in order to further optimize the image quality, three image enhancement technologies, including histogram equalization, contrast enhancement and image sharpening, are combined to cope with visual interference in different environments, enhance the contrast and detail expressive force of the image, and further improve the image processing effect. Therefore, the unmanned aerial vehicle identification system can capture and identify the hostile target more accurately.

The invention relates to the technical field of underwater image enhancement, and provides a deepwater image enhancement method based on multi-color space coupling, which comprises the following steps: determining an optimal channel, a moderate attenuation channel and a severe attenuation channel in an RGB color space based on a pixel average value, carrying out asymmetric histogram cutting pre-correction on the optimal channel, and carrying out deep-water image enhancement on the optimal channel; designing a loss function to enable an attenuation channel to approach an optimal channel through iterative compensation; in the Lab color space, clustering and segmenting the brightness component L into a plurality of pixel blocks by using an unsupervised pixel clustering model PCM, and performing local contrast enhancement and guided filtering-based noise reduction processing on each pixel block; and finally, carrying out nonlinear stretching on the chrominance components a and b by adopting a self-adaptive exponential function to improve the color naturalness. Through a multi-color space cooperative enhancement mechanism, the limitation of single color space processing in a traditional method is effectively solved, and the color correction precision, brightness enhancement and noise reduction balance and the naturalness optimization effect of the deep water image are improved.

The invention provides a compression condensing unit state monitoring method based on anomaly detection and a storage medium, and relates to the technical field of industrial equipment operation state monitoring. The method comprises the specific steps that multi-source time sequence data is collected to construct a compression condensing unit state data set, a thermal disturbance matrix is generated based on difference and thermosensitive weight weighting, and a sound and vibration superposition disturbance mapping value is formed through a frequency band interval and a sound and vibration sensitive factor; constructing a collaborative variation tensor by using an asymmetric disturbance difference score, a residual range response value and a collaborative perception factor, and generating a multi-polarity contrast enhancement value in combination with a time modulation period, a disturbance suppression factor, an adaptive projection weight and a direction enhancement item; on this basis, a folding manifold value and a structure partition memory kernel anomaly score are formed and mapped into an anomaly probability; the compression condensing unit anomaly detection model is trained through weighted cross entropy loss, the anomaly detection accuracy and reliability are improved, and anomaly detection of the compression condensing unit is achieved.

The invention provides an adaptive contrast enhancement video fusion algorithm for a complex logistics scene, and the algorithm comprises the steps: extracting environment features from an initial video data set, analyzing the inter-frame illumination change and object movement speed through employing a convolutional neural network, determining that the current scene is a peak period or a low-illumination environment, and obtaining environment change perception parameters; extracting a key frame from the second video data set, separating a foreground object and a background by adopting an image segmentation technology based on deep learning, and performing detail retention enhancement on the foreground object to obtain a third video data set; real-time verification is carried out on the final video output, if the processing delay exceeds a preset threshold value, the resource distribution proportion is adjusted through a feedback mechanism, mode switching and enhancement processing are executed again, and the updated video output is obtained.

The invention proposes a liver fibrosis prediction method based on medical image recognition, and relates to the technical field of image recognition, and the method comprises the steps: S1, carrying out the backtracking collection of a multi-modal medical image set from the historical medical record data of definite fibrosis, and enabling the medical image set to correspond to the definite fibrosis degree, so as to generate an image-label pair; s2, preprocessing the medical image set, including denoising, contrast enhancement and liver region segmentation; s3, a prediction model is constructed, the prediction model takes a multi-path attention U-Net network as a framework, the preprocessed medical image set is divided into a training set and a verification set, iterative training is carried out on the prediction model until a loss function converges, and the trained prediction model is obtained; and S4, obtaining a new medical image, and inputting the new medical image into the trained prediction model to obtain a liver fibrosis prediction result. The method can overcome the problems of separation of segmentation and classification tasks, insufficient evaluation precision and the like in the prior art.

The invention discloses a tea leaf fermentation degree determination method with image recognition, which comprises the following steps: shooting tea leaves through a high-definition camera and a camera in different tea leaf processing stages according to different tea leaves to obtain high-definition images of the tea leaves, preprocessing the collected tea leaf images, and determining the fermentation degree of the tea leaves according to the preprocessed tea leaf images. Through denoising, contrast enhancement and color correction technologies, an acquired image is preprocessed, the image is converted into a proper color space for extracting color features in the tea image, and dominant hue and color distribution on the surface of tea are analyzed. The invention relates to the technical field of tea leaf detection, and the method can realize non-destructive, real-time and accurate tea leaf fermentation degree monitoring, and automatically judges the fermentation state of tea leaves by shooting tea leaf images and analyzing visual features of colors, forms and textures of the tea leaves in combination with a machine learning algorithm. Therefore, the automation level and the production efficiency of the tea processing process are improved.

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

The invention discloses a multi-modal recommendation method based on hypergraph edge diffusion, which comprises the following steps: firstly, collecting user-project interaction and multi-modal features, constructing a multi-source relation graph and generating a modal perception hyperedge; then, optimizing a hypergraph structure through forward diffusion and reverse denoising; performing feature learning in combination with local graph convolution and a global hypergraph attention network; then, cross-modal contrast enhancement is introduced to improve feature consistency; and finally, sorting recommendation is realized through joint optimization of recommendation loss, diffusion loss and comparison loss. According to the multi-modal recommendation method based on hypergraph edge diffusion, a modal perception hypergraph structure and a diffusion generation mechanism are introduced, so that the problem of recommendation performance reduction caused by sparse user-project interaction can be effectively relieved, and modal information and a high-order user-project relationship can be fully mined; and the recommendation accuracy and the model robustness in a data sparse environment are improved.

The invention belongs to the technical field of image enhancement and recognition, and particularly relates to a radar image intelligent enhancement and recognition method and system based on multi-model fusion, and the method comprises the steps: carrying out the adaptive suppression of speckle noise of an original radar image; feature point detection is carried out, robust transformation matrix estimation and adaptive contrast enhancement are carried out, and a corrected and enhanced image is output; utilizing the generative adversarial network and multi-loss function collaborative constraint to obtain a texture reconstruction image; establishing an image-semantic double-flow network architecture, performing cross-modal attention fusion to obtain a fusion feature map, and outputting a target recognition result; performing time phase division on the texture reconstruction image, judging a change type, and outputting a change detection result; and outputting a processing report including the enhanced image, the target list and change analysis. According to the method, noise suppression, correction enhancement, texture reconstruction, target recognition and change detection are integrated, the defect of fragmentation processing in the traditional technology is overcome, and the overall processing performance and the actual application adaptability are improved.

The invention provides a fan blade surface defect detection method and system based on an unmanned aerial vehicle visual image. The invention relates to the technical field of defect detection, and the method comprises the following steps: acquiring a surface image sequence of a high-speed rotating blade, and synchronously acquiring blade movement track data; performing space-time correlation on the motion trail data and the image sequence, and compensating blade edge distortion frame by frame; dynamically distributing contrast enhancement weights for the windward side and the leeward side of the blade in the compensated image sequence according to the rotating speed, and generating a fused texture feature map; the knowledge distillation attention weight is adjusted according to the distribution proportion of high-frequency vibration and a static region in the feature map, and high-precision model features are migrated to a lightweight model; and embedding a blade motion state vector in the lightweight model, dynamically adjusting a detection sensitivity threshold, and outputting a defect result matched with the motion state. The problems of dynamic and static characteristic unbalance and high false detection rate of blade defect detection in a high-speed rotation scene can be solved.

The invention relates to a self-adaptive underwater image enhancement system and method suitable for medium-deep water turbid scenes, and belongs to the technical field of underwater image enhancement. Comprising the following steps: S1, selecting a reference channel based on RGB channel pixel mean value distribution of an underwater image, performing adaptive color compensation on an attenuation channel, and automatically optimizing compensation parameters through a particle swarm optimization algorithm; s2, global contrast enhancement and local contrast enhancement are carried out on the image after color compensation, the global contrast enhancement adopts an improved histogram double-end stretching method, and the local contrast enhancement adopts a CLAHE algorithm combined with guiding filtering noise reduction; and S3, performing multi-scale fusion on the global contrast enhancement result and the local contrast enhancement result based on an image pyramid technology, and generating a final enhanced image through weight distribution and layered reconstruction. A self-adaptive color channel compensation formula is provided, and self-adaptive color compensation is performed on channels which are easy to attenuate, so that the problem of color cast is solved, and the visual effect of an image is remarkably improved. By further enhancing the contrast and improving the texture of the underwater image after the color cast is improved and utilizing an image pyramid technology to realize multi-scale fusion, the problems of blurring and low contrast caused by underwater scattering particles in the prior art are solved.

The invention relates to the technical field of part surface defects, in particular to an industrial part surface defect recognition system and method, and the system comprises a high-resolution industrial camera which is used for collecting an industrial part surface image and is provided with an automatic focusing and light source compensation module; therefore, clear images can be obtained in different light environments; the image preprocessing unit has the functions of denoising, contrast enhancement and edge detection and ensures the saliency of defect features in the original image; a defect detection model based on deep learning, wherein the model is designed through a convolutional neural network structure and can automatically identify and classify various surface defects including but not limited to scratches, pits, cracks, air holes and the like; the defect positioning module is used for accurately marking the specific position of the defect on the surface of the part in combination with an image processing algorithm and calibration information; the system further comprises a data storage and management unit, a system control unit and an abnormity alarm module.

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.