306 results about "Histogram equalization" patented technology

The invention relates to the technical field of image processing, in particular to an anti-collision beam weld defect detection method and system based on image processing, and the method comprises the steps: carrying out the image collection of a weld region of a produced anti-collision beam, and obtaining a gray image; the method comprises the following steps: performing initial partitioning on a grayscale image, respectively obtaining a local complexity index of each initial sub-block, obtaining at least two adaptive sub-blocks based on the local complexity index of each initial sub-block, and performing adaptive local histogram equalization on each adaptive sub-block to obtain a target grayscale image; the method comprises the steps of performing edge detection on a target grayscale image to obtain at least two edge pixel points, performing frequency domain conversion on the target grayscale image according to a gradient direction of each edge pixel point to obtain a frequency domain image, performing filtering processing and time domain conversion on the frequency domain image to obtain a denoised image, and identifying defects in the denoised image by using a neural network. And the defect detection efficiency is improved by inhibiting the periodic texture in the weld seam image.

The present invention relates to the field of rock structure measurement, and disclosed are a three-dimensional pore reconstruction method and system based on a rock image, for use in solving the problem that using a machine learning method to reconstruct pores in a rock image requires relatively high mathematical or computer science expertise, involves high labor and economic costs, and is difficult to apply to small-scale rock pore reconstruction projects. The method comprises: step 1: grouping slice images of each rock sample into one set, and performing preprocessing; step 2: performing grayscale processing, histogram equalization and normalization processing on an image; step 3: using a Harris corner detection algorithm to perform corner detection, sorting Harris response values, selecting key points by setting the number of key points, and using a non-maximum suppression method to screen the selected key points; step 4: segmenting the boundaries of pores in the image by means of an adaptive threshold selection method, and extracting pore features; and step 5: using a marching cubes algorithm to construct a three-dimensional pore reconstruction model. The present invention is used for three-dimensional reconstruction of pores in a rock image.

The invention relates to the field of image processing, in particular to a pipeline wall defect detection method and system based on image recognition, and the method comprises the steps: obtaining a preprocessed gray-scale map of a pipeline wall; calculating a segmentation demand degree of each pixel point in the grey-scale map, dividing the grey-scale map into a plurality of sub-image blocks by using the segmentation demand degrees, and performing histogram equalization on each sub-image block to obtain an enhanced grey-scale map; and inputting the enhanced grey-scale map into a pre-trained neural network model, and outputting a pipeline wall defect detection result. According to the method, the grey-scale map is dynamically divided according to the segmentation demand degree of the pixel points, and the image can be divided into the sub-image blocks which better conform to the defect features, so that the problem of defect feature loss or misjudgment is avoided, and the defect detection accuracy is improved.

The invention belongs to the technical field of image processing, particularly relates to an image effect enhancement method and system, and aims to improve the image quality through multi-domain collaborative adaptive processing. The method comprises the following steps: firstly, determining an optimal quantization level through content-aware adaptive quantization, generating a quantization image and a region complexity graph, then decomposing the image into smooth, edge and texture sub-bands through multi-scale domain analysis, constructing a characteristic spectrum, then, applying histogram equalization, anisotropic diffusion and nonlinear sharpening to different sub-bands through adaptive characteristic enhancement, and finally, carrying out adaptive characteristic enhancement on the different sub-bands. And residual error optimization is carried out. And finally, multi-domain collaborative fusion is carried out to construct an enhancement matrix, fusion masks and feature correction are applied to obtain a final enhanced image, and the method is suitable for various images, retains details, enhances different regions in a targeted manner, and has a remarkable effect.

The invention relates to the technical field of machine vision, in particular to a precision part size automatic detection method and system based on machine vision, precision part images are collected through a high-precision industrial camera, part positioning is carried out, sub-pixel-level topological feature mapping is carried out on interested area images, and precision part size automatic detection is carried out. Comprising the steps of gray histogram equalization, gray morphological processing, edge detection and edge chain code tracking, construction of an edge point topological feature space, execution of sub-pixel subdivision, obtaining of an edge line through contour analysis of a feature distance and a feature angle, and double-constraint geometric reconstruction based on the edge line. The characteristic distance and the characteristic angle are used for rotation matrix conversion and geometric dimension calculation, a relation model of the geometric dimension and the actual dimension of the part is established, precise part dimension measurement is achieved through dynamic error analysis and compensation, the measurement precision is remarkably improved, the risk caused by unreliability of a single characteristic is effectively reduced, and the measurement accuracy is improved. And the measurement stability is improved.

The invention discloses a photovoltaic module infrared image fault detection method based on unmanned aerial vehicle inspection, which relates to the field of fault detection, and comprises the following steps: configuring an unmanned aerial vehicle platform, planning a flight path, setting aerial photography parameters, setting an infrared thermal image acquisition and infrared image data return and storage mechanism, and performing gray normalization processing, image noise reduction, histogram equalization, edge enhancement processing and size standardization processing on the infrared image data, and performing data enhancement operation. An unmanned aerial vehicle infrared inspection technology is combined with a deep learning target detection model, a set of complete photovoltaic module infrared image fault detection process is established, and full-process automatic processing from image acquisition, image preprocessing, model detection to result evaluation and visualization can be realized. The method has the comprehensive advantages of being high in fault recognition precision, high in detection speed, standardized in processing flow and the like, and the efficiency and the intelligent level of photovoltaic power station component-level fault inspection are remarkably improved.

The invention relates to the technical field of image processing, and provides an image enhancement method and device and electronic equipment, and the method comprises the steps: carrying out the filtering processing of a to-be-enhanced image through employing a multi-scale low-pass filter with all-passband flat response, carrying out the dynamic range compression of the filtered image, and obtaining a multi-scale enhanced image; based on histogram distribution characteristics of the multi-scale enhanced image, dividing a pixel value range of the multi-scale enhanced image into a plurality of sub-intervals, and performing gray boundary truncation processing on each sub-interval to obtain a multi-scale interval image; dynamically determining a window size based on the scale feature of the multi-scale interval image, and performing multi-scale adaptive contrast limit histogram equalization on the multi-scale interval image based on the window size to obtain a multi-scale equalization image; and fusing the multi-scale equalized image to obtain a final enhanced image. According to the image enhancement method and device and the electronic equipment provided by the invention, the image enhancement effect is effectively improved.

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 discloses a skin image super-resolution conversion system and a skin image super-resolution conversion method based on deep learning, and relates to the technical field of image super-resolution conversion, and the system evaluates whether illumination is qualified or not by collecting skin near-illumination and analyzing illumination conditions of the skin near-illumination, establishing a light environment analysis model and calculating a light influence coefficient. The method comprises the following steps: preprocessing qualified images, including color space conversion, histogram equalization, edge detection and denoising; thirdly, evaluating the quality of image pixels, calculating a plurality of pixel features, judging whether the pixel features conform to the standard or not, and performing optimization through strategies such as sharpening and de-noising; a multi-scale feature extraction network and a self-attention mechanism are adopted to extract image features, a cross-modal mapping model is constructed, super-resolution conversion is performed through a generative adversarial network, and image details and definition are improved. And finally, monitoring the quality of the super-resolution image, evaluating the conversion effect, and adjusting the processing strategy according to the quality coefficient to ensure that the output image meets the medical diagnosis requirement.

The invention provides a defect visual identification detection method for dense welding spots, which comprises the following steps: collecting a high-density welding spot array image, carrying out hierarchical processing on the image by adopting wavelet transform, separating out feature information containing an edge overlapping region and shadow distribution, and obtaining a preliminary welding spot region distribution diagram; according to the preliminary welding spot area distribution diagram, performing feature enhancement processing on edge overlapping areas in the distance between adjacent welding spots by adopting a Laplace operator to obtain an enhanced welding spot boundary image; for the enhanced welding spot boundary image, performing fuzzy processing on shadow boundaries in a distance range of adjacent welding spots, separating contour information of a single welding spot through an adaptive threshold segmentation method, and determining an individual region range of the welding spots; and extracting image features of surface texture, edge integrity and shape regularity of the welding spots from the determined individual region range, and performing normalization processing on gray distribution of each welding spot region by applying a histogram equalization method to obtain corrected welding spot feature data.

The invention discloses a collected image recognition method and system for a surveying and mapping unmanned aerial vehicle, surveying and mapping image data and surveying and mapping point cloud data of a target surveying and mapping area are obtained through a multi-mode sensor carried by the unmanned aerial vehicle, and meanwhile, a time camera is used for capturing a motion signal of a dynamic target to generate event stream data; histogram equalization and multi-scale enhancement are carried out on the surveying and mapping image data, and voxel filtering and curved surface reconstruction are carried out on the surveying and mapping point cloud data; wavelet packet decomposition is utilized to extract frequency domain features of an image in the preprocessed acquisition data, and the frequency domain features are fused with motion trail features in event stream data; and inputting the mixed feature vector into a lightweight adaptive model, dynamically adjusting weights of different modes through an attention mechanism, and outputting a surveying and mapping result of the target surveying and mapping area. The information of the image in different frequencies and directions can be described more comprehensively and meticulously, and the surveying and mapping efficiency is effectively improved.

The invention relates to the technical field of industrial automation quality control, in particular to a bicycle lifting seat production defect detection method and system based on machine vision, and the method comprises the steps: carrying out the illumination decomposition and highlight reconstruction of collected surface data, and generating a balanced texture map with uniform illumination through a gradient domain local repair algorithm and global histogram equalization. Then, the balanced texture map is input into a two-channel parallel analysis architecture comprising a linear flaw attention network and a regional heterogeneity analysis network, and probability maps of scratch defects and oxidation defects are extracted; then, performing spatial correlation intelligent arbitration and weighted fusion based on a local confidence mean value on the two paths of probability graphs so as to eliminate overlapping conflicts and background noise, and generating a fusion defect graph; and finally, carrying out topology and geometric constraint filtering on the fused defect graph, normalizing the defect form, and finally outputting a defect positioning mask representing the position and contour of the defect. According to the invention, identification and distinguishing of surface scratches and oxidation defects of the bicycle lifting seat are realized.

The invention provides a wooden furniture defect detection method based on image recognition, and the method comprises the steps: collecting a furniture surface image and environment information through an industrial camera and an illumination sensor, carrying out the graying, histogram equalization and Gaussian filtering preprocessing, extracting multi-scale features through a convolutional neural network, and carrying out the detection of the defects of the furniture surface image and the environment information; a defect area is positioned in combination with a space attention mechanism, and then the distinction degree of different defect categories is improved through a predefined priority knowledge base and a semantic attention module. And combining space and semantic double attention mechanisms to generate a weight matrix, realizing dynamic redistribution of defect confidence, adding local contrast regularization loss, optimizing response sorting of the model on a multi-defect sample, and finally outputting a defect detection result according to priority and confidence. According to the method, the main defect identification accuracy, the confidence coefficient distribution rationality and the integrity and robustness of overall detection in a multi-defect environment are improved.

The invention discloses a residual capacity balance control method and system for an aged lithium battery, and relates to the technical field of new energy materials and devices, and the method comprises the steps: inputting quantum magnetic field response data and infrasonic wave signals into a single-chip microcomputer, carrying out the wavelet noise reduction and histogram equalization processing based on an STM32 single-chip microcomputer, and generating an acoustic-magnetic coupling feature matrix; inputting a dynamic pheromone equalization model based on the acoustic-magnetic coupling characteristic matrix, calculating a capacity difference coupling coefficient between the battery packs through an ant colony optimization algorithm, and generating a capacity redistribution path topological graph; according to the capacity redistribution path topological graph, the energy transfer direction is controlled through a bidirectional Buck-Boost circuit, and an equalizing current threshold value is dynamically adjusted based on the quantum spin resonance magnetic field gradient; according to the invention, the quantum magnetic field response data of the aged lithium battery pack and the infrasonic wave signal generated by electrolyte decomposition are collected, so that the physical and chemical states in the battery are accurately monitored.

The invention discloses a high-frequency and high-speed circuit board detection method based on infrared image enhancement and mode recognition, and relates to the technical field of circuit board defect detection, and the method comprises the steps: carrying out the non-contact infrared image collection of a high-frequency and high-speed circuit board which is electrified to work through a thermal infrared imager, and obtaining the original infrared image data containing the temperature distribution of each region of the circuit board; performing enhancement processing on the infrared image by applying histogram equalization and filtering denoising algorithms, and highlighting a tiny thermal change region; and extracting heat distribution characteristics from the enhanced infrared image to obtain a heat distribution characteristic sequence table. By combining infrared image enhancement and mode recognition and adopting a non-contact detection mode, physical damage to the circuit board caused by traditional contact probe detection is avoided, the integrity of the circuit board is protected, the service life of the circuit board is prolonged, the defective rate caused by detection is reduced, and the detection efficiency is improved. The method is particularly important for high-precision and high-value products such as high-frequency and high-speed circuit boards.

The invention discloses a deformation monitoring method and system based on a machine vision technology, and relates to the technical field of crossing of structural health monitoring and computer vision, and the method comprises the steps: improving the angular point positioning precision in a calibration stage through a sub-pixel-level Harris angular point detection algorithm; robust recognition and initial positioning of an artificial marker in a first frame of image are realized by using an X-Feat convolutional neural network model, an anti-interference template is generated in combination with histogram equalization and Gaussian filtering, the stability of the system is enhanced, a local search area is delimited by taking an initial positioning point as a center, and a deep learning and interpolation algorithm is combined, so that the anti-interference performance of the system is improved. High-precision tracking and displacement calculation of the marker center are realized, the monitoring resolution is effectively improved, pixel-level displacement is accurately converted into physical space displacement through inverse transformation of a homography matrix, and dual threshold criteria of mode length and change rate are introduced for dynamic early warning, so that the engineering practicability and safety response capability of a monitoring result are improved.

The invention relates to the technical field of image data processing, in particular to a boiler coke accumulation detection system based on unmanned aerial vehicle inspection, the system comprises a processor and a memory, and the processor executes a computer program of the memory to realize the following steps: obtaining at least two correction images and one fusion image of any to-be-detected part in a boiler, if it is detected that a suspected focus area exists in the fused image, a reference area corresponding to the suspected focus area is obtained from each corrected image, and the corrected image is obtained according to the gray value difference of pixel points in the suspected focus area and each reference area, the gray value change features of the pixel points in the suspected focus area and the gray value difference. Obtaining the focus accumulation confidence degree of each suspected focus accumulation area in the fused image; according to the focus accumulation confidence degree of each suspected focus accumulation area, the total weighted frequency number of each gray value in the fused image is obtained, histogram equalization is utilized to carry out enhancement processing on the fused image, an equalized image is obtained, and the accuracy of focus accumulation detection is improved.

The invention provides an industrial visual imaging method and system suitable for a high-humidity water vapor environment and a medium, and the method comprises the steps: obtaining temperature fluctuation data and humidity fluctuation data in the water vapor environment at different time nodes based on a temperature and humidity sensor, and analyzing the environment parameters of a shooting environment; acquiring a shot image, analyzing brightness values of pixel points of the shot image, and performing equalization processing on the brightness values to obtain a brightness average value; analyzing fog concentration information of the shooting environment based on the environment parameters of the shooting environment, and analyzing interference information on the brightness mean value based on the fog concentration information; switching a shooting mode based on the interference information, and carrying out defogging processing on the shot image; carrying out enhancement processing, carrying out number domain stretching on the enhanced image, and then executing histogram equalization processing to obtain a visual imaging image; the shooting mode is adjusted by analyzing the interference information of the fog concentration on the image brightness, the image is defogged, the image is enhanced to recover image details, and the recognition capability is improved.

The invention relates to a pathological section splicing method, system and device and a storage medium, and the method comprises the steps: collecting a pathological section image, converting the pathological section image into a plurality of modes, and carrying out the histogram equalization and denoising processing; calculating an entropy value of each pixel point region in the image, and extracting pixel points with high entropy values as feature points; carrying out preliminary matching on the image under different scales, and carrying out bidirectional matching operation in a matching region to obtain a feature point pair; constructing a deformation model according to the feature point pairs, calculating parameters of the deformation model, and enabling the to-be-registered image to deform based on the deformation model through minimizing deformation energy; gradient information of an image overlapping region is calculated, a weight is allocated to each pixel point in the overlapping region according to a weight allocation rule, and the edge of the image overlapping region is processed to obtain a spliced image; and carrying out quantitative evaluation on the spliced image. According to the method, the problems of low registration precision, large calculation amount and poor adaptability to complex backgrounds in the prior art can be solved.

The invention discloses a crown instance segmentation and maturity evaluation method based on dynamic expansion convolution. The method comprises the following steps: firstly, carrying out radiation correction, geometric correction and histogram equalization preprocessing on a forest RGB image acquired by an unmanned aerial vehicle; then extracting global contour features through an adaptive dynamic expansion convolution module, enhancing local detail capture capability in combination with an edge perception feature pyramid network, and realizing multi-level feature fusion by using a double attention mechanism; then, a density adaptive contour cross suppression algorithm is adopted to optimize the mask, and the distinguishing precision of the dense area is improved through dynamic adjustment of a suppression threshold value and ray method contour cross calculation; and finally, calculating canopy density based on a segmentation result, and constructing a maturity index model by fusing the shape, color and geometric features of the crown breadth. According to the method, the extraction capability of the sub-pixel-level details of the crown edge is remarkably improved, the problems of fuzzy irregular contour segmentation and high-density region misjudgment are effectively solved, and an efficient and accurate technical scheme is provided for forestry resource monitoring.

The invention belongs to the technical field of image processing, and discloses a cable tube well hole site occupation detection method based on multi-view image fusion, which comprises the following steps of: selecting a fixed position in a cable tube well, and acquiring images of the same well wall section from upper, lower, left, right and front five views by using handheld imaging equipment; performing guided filtering denoising and contrast-limited histogram equalization processing on the image to enhance the image quality; extracting a contour and calculating circularity, and determining a candidate circle center through a segmented recursive circle center positioning method; cross-view matching and verification are carried out on the holes with shielding, feature restoration is realized through centroid alignment and curvature similarity calculation, and the hole position state is judged by adopting a majority decision principle; and finally, performing joint decision-making based on the spatial positions, direction consistency and texture features of the cable and the hole, and generating and outputting an occupancy state matrix. The method effectively improves the recognition precision and robustness in a complex shielding environment, and is suitable for power resource management.

The invention provides a plastic toy surface defect detection method based on machine vision, and the method comprises the steps: firstly collecting and standardizing a plastic toy surface multi-parameter original image, and carrying out white balance correction and histogram equalization to improve the image quality; secondly, decomposing image frequency domain features by applying multi-scale wavelet transform, and enhancing defect area spatial positioning and texture sensing capabilities by combining cross guidance of frequency domain and spatial domain attention mechanisms; and inputting the fused features into a lightweight convolutional neural network to extract a high-discrimination feature vector, executing defect classification and position regression, and introducing an adaptive mechanism to dynamically optimize a decomposition scale and an attention strategy according to recognition confidence and detection history. The method is suitable for automatic surface quality detection under a complex background.

The invention relates to the technical field of glass bottle detection, and discloses a glass bottle bottom defect detection method which comprises the following steps: building a detection platform, and adopting a multi-view imaging module of an annular LED and oblique light supplement combined light source, a vertical camera and 3-4 oblique cameras; after the to-be-detected glass bottle is positioned, synchronously collecting a bottle bottom full-view image set; graying, adaptive median filtering, CLAHE histogram equalization and Otsu binarization preprocessing are carried out on the image; extracting geometric and textural features of the image, inputting the geometric and textural features into an improved YOLOv5 model for identification and classification, and judging defects by combining with multi-view result fusion; and sorting the glass bottles according to a detection result and generating a traceable report. The identification rate of tiny defects is larger than or equal to 99%, the detection time of a single bottle is smaller than or equal to 0.5 second, and the method is compatible with glass bottles with the diameter of 30-100 mm, is suitable for large-scale quality control in the fields of food and beverage, medicine packaging and the like, and has extremely high practical value.

The invention relates to the technical field of data processing, and discloses a pipeline corrosion repairing method and system based on artificial intelligence and a storage medium. The method comprises the following steps: acquiring a corrosion image through a pipeline endoscope, and obtaining a standardized feature data set through Gaussian filtering and histogram equalization processing; inputting a semantic segmentation network for identification and segmentation to obtain corrosion three-dimensional coordinates and volume data; according to the coordinates and chemical factors, mortar proportioning parameters are obtained through calculation; inputting the volume data into a depth-thickness mapping network to obtain repair thickness distribution; and jet parameter setting and construction control are conducted according to the thickness distribution, and pipeline corrosion repairing is completed. The technical problems that in an existing pipeline corrosion repairing method, corrosion recognition precision is low, repairing parameters lack pertinence, multi-point corrosion collaborative optimization is insufficient, and the intelligent degree of construction control is low are solved.

The invention relates to the technical field of medical image processing, in particular to an embolism focus segmentation method and system based on medical image processing. The system comprises a medical image feature registration module, an image frequency band fusion enhancement module, a deep network lesion segmentation module and a lesion segmentation image fusion optimization module, an embolism CT image and an embolism MRI medical image can be acquired, and feature point minimization registration and image frequency band fusion are performed to generate an embolism fusion image; performing histogram equalization processing on the embolism fusion image to generate an embolism comparison standard image; constructing a corresponding deep network lesion segmentation model, and performing network lesion segmentation processing, up-sampling and element-by-element splicing fusion to generate an embolism lesion feature fusion image; and performing morphological optimization operation on the lesion boundary corresponding to the embolism lesion feature fusion image to obtain an embolism lesion segmentation result. According to the invention, accurate segmentation of the embolism focus in the medical image can be realized.

The invention discloses a low-illumination blurred image 3D scene reconstruction method based on Gaussian sputtering, and the method comprises the steps: S1, building a progressive iteration enhancement frame, and setting a middle brightness anchor point between low-light observation and target brightness; s2, generating a plurality of enhanced images based on the intermediate brightness anchor points in combination with histogram equalization and gamma correction technologies; s3, carrying out rapid deblurring processing on the enhanced image; s4, constructing a scene representation model based on 3D Gaussian sputtering, and performing explicit estimation and noise suppression in combination with a noise sensing module; s5, taking the reconstructed rendered image as the deblurring prior of the enhanced image of the next brightness level so as to execute deblurring processing operation, and performing iterative optimization until the target brightness is reached; and S6, generating a high-quality new view angle image based on the finally reconstructed 3D scene. According to the method, the rendering speed is greatly improved while the reconstruction quality is ensured, real-time three-dimensional reconstruction is realized, and the problem of noise amplification in a low-light environment is effectively solved.

The invention discloses an aluminum bar surface quality detection method in the field of aluminum bar production, and the method comprises the following steps: S101, image collection: employing a high-definition line-scan digital camera to move at a constant speed along the axial direction of an aluminum bar for shooting, and cooperating with a multi-angle annular light source to enhance the surface defect contrast, and obtaining a continuous image of the surface of the aluminum bar; step S102, image preprocessing: sequentially carrying out graying processing, median filtering denoising and histogram equalization enhancement on the acquired image; through high-definition image acquisition, an advanced image processing algorithm and a machine learning classification model, tiny defects on the surface of the aluminum bar can be accurately detected, the defect type and severity can be accurately judged, the detection accuracy and reliability are improved, an automatic image acquisition and processing system is adopted, and the detection efficiency is improved. The surface of the aluminum bar can be rapidly detected, compared with manual visual detection, the detection efficiency is greatly improved, and the real-time detection requirement on a large-scale production line can be met.

The invention discloses a radar anti-interference method based on frequency domain modeling and image collaboration, and the method comprises the steps: S10, carrying out the preprocessing of an original echo, and finally packaging a preprocessing result into a three-order tensor form; s20, performing FNO interference feature extraction and identification, and outputting interference type probability distribution and an interference-to-signal ratio estimation value through a de-wharf; s30, selectively processing the image, and reconstructing the histogram into an SAR image in an equalization manner; s40, evaluating the credibility of the image, and outputting an imaging credibility score between 0 and 1 through a regression head; s50, generating a credibility-interference combined criterion for judging the reliability of the radar guidance state; s60, a loss function is designed, back propagation is carried out through a total loss function, the model gradient is updated, and model parameters are optimized; and S70, dynamic anti-interference strategy matching: retrieving candidate anti-interference actions from a preset strategy library based on the interference types, performing grading and sorting according to complexity, and when the joint risk score is lower than a set threshold value, activating the optimal high-complexity anti-interference process of the corresponding interference type.

The invention relates to the technical field of image processing, and discloses an underwater image enhancement method based on sparse prior guidance and frequency domain information fusion, which comprises the following steps: constructing and training an underwater image enhancement network, inputting an original image into a red channel histogram equalization prior guidance network, and obtaining a prior weight map; a sparse priori fusion module is adopted to analyze the priori weight map and capture the context relation between global pixels of the image, priori flow branches are utilized to reinforce image color information, feature flow branches are utilized to expand channel dimensions to carry out image local refinement, and finally a feature map is extracted; and inputting the feature map into a wavelet edge fusion module, and cascading an edge enhancement network to obtain an enhanced underwater image. The red channel histogram equalization prior guidance module provided by the invention can effectively solve the problem of color distortion of an underwater image caused by insufficient brightness of a red channel, and the wavelet fusion module is utilized to refine image edge information and improve image detail information.

The invention relates to a distribution line unmanned aerial vehicle inspection image fusion method and system and a medium, and the method comprises the steps: carrying out the aerial inspection of a distribution line through a high-resolution visible light camera and an infrared thermal imaging camera carried by an unmanned aerial vehicle, and obtaining the image data of a key part; performing denoising processing on the acquired multi-modal image to reduce interference caused by environmental factors and sensor noise, and then enhancing the image contrast and improving the definition of image details through a histogram equalization method to obtain denoised and enhanced intermediate preprocessing data; according to the method, a power distribution line unmanned aerial vehicle image fusion model composed of an encoder and a decoder is constructed, image fusion is performed on source images of different modalities after denoising and enhancement, so that data advantages of different modalities are fully utilized, richer result images are generated, more comprehensive line state information can be provided, and the reliability of the power distribution line unmanned aerial vehicle image fusion is improved. More accurate state evaluation and fault diagnosis are realized, the inspection efficiency and accuracy are improved, the cost is reduced, and the safety is improved.