174 results about "Histogram equalization" patented technology

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 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 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 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 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 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 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.

The invention relates to the technical field of agricultural equipment control, in particular to an intelligent fusion control navigation system of a vegetable collection and transportation all-in-one machine, which comprises an image acquisition and preprocessing module, a cabbage ridge line identification and positioning module, a multi-source information fusion decision module and a path tracking and cooperative control module. The method comprises the following steps: collecting ridge row images through a visual sensor, executing multi-scale fusion denoising, histogram equalization and perspective correction, and extracting a clear visual navigation datum line; gNSS and IMU data are fused with visual information, and extended Kalman filtering is adopted to realize high-precision pose estimation; switching different operation modes based on the operation state machine, and dynamically generating a corresponding expected path; path tracking control is completed in combination with a pure tracking algorithm and a speed mapping table, and intelligent cooperative control over the whole receiving and operating process is achieved. According to the method, the ridge row identification robustness, the path tracking precision and the system collaborative operation capability are improved, and the method is suitable for automatic harvesting operation of cabbages in a complex farmland environment.

The invention relates to a multi-cavity injection mold insert defect identification method, which comprises the following steps: acquiring a high-resolution image through an industrial camera, and carrying out graying, de-noising, histogram equalization and other preprocessing to improve the image quality. Then, an edge thermodynamic diagram is generated in combination with Canny edge detection and a deep learning model, a multi-channel segmentation network is guided to extract multi-scale features and dynamically enhance edge response, and continuity and accuracy of defect boundary detection are improved by means of an adaptive feature fusion module. Furthermore, the defect area is repaired through dynamic threshold segmentation and morphological operation, and finally visual labeling and automatic output of the high-precision defect area are achieved. According to the method, parameters can be adaptively optimized to deal with image blurring and other situations, a perfect model parameter adaptation mechanism is provided, and the defect recognition robustness and engineering applicability under complex working conditions are remarkably improved.

The invention relates to the technical field of image data processing, in particular to an impurity identification method and system for fruit and vegetable powder, and the method comprises the steps: collecting a fruit and vegetable powder image on a conveying belt, preprocessing the image into a gray-scale map, and calculating a dark channel index based on the gray-scale distribution characteristics in a neighborhood window to preliminarily screen a dark region; counting dispersion of gradient amplitude and information entropy of gradient direction in a pixel point neighborhood window, distinguishing smooth shadow and rough impurities by using the dispersion and the information entropy, and correcting a dark channel index to obtain impurity confidence; constructing a local histogram by using the impurity confidence as a weight, and carrying out adaptive weighted histogram equalization processing on the grey-scale map to enhance the impurity contrast and suppress the shadow; and completing impurity identification through Otsu threshold segmentation and connected domain analysis. According to the method, the problem that shadow is easily misjudged as impurities when accumulated powder is processed by a traditional algorithm is effectively solved, and the detection accuracy is improved.

The invention provides a target detection method and device based on multi-modal data fusion, and relates to the technical field of image processing, and the method comprises the steps: carrying out the ternary weighting calculation of visible light data and infrared spectrum data, so as to determine an optimal matching relation, and generating a video frame sequence; constructing a Laplacian decomposition structure according to the video frame sequence, executing weight fusion layer by layer to form cross-scale fusion data and generate a dark channel, estimating an atmospheric light value and transmissivity to recover a smog-free image, and executing local contrast limited histogram equalization processing and dynamic brightness adjustment to obtain an enhanced image; applying a dynamic convolution structure, a deformable convolution structure and a spatial pyramid structure to the enhanced image to extract multi-scale key features; and taking the multi-scale key features as attention calculation input, and processing the enhanced image based on a double-layer routing attention structure to output a target detection result. According to the invention, the accuracy of target detection under the smog shielding condition can be improved.

The invention relates to the technical field of power equipment monitoring, and discloses a GIS switch equipment operation state evaluation method and system based on image analysis, and the method comprises the steps: collecting an initial image of GIS switch equipment through employing a high-definition camera, an infrared thermal imager and an ultraviolet imager; carrying out denoising processing on the collected initial image by adopting a self-adaptive median filtering algorithm, and carrying out enhancement processing on the denoised image by adopting a mode of combining histogram equalization and CLAHE to obtain a preprocessed image; inputting the preprocessed image into a feature extraction model, splicing the extracted feature vectors into a multi-dimensional feature matrix, and dynamically adjusting the weight of the features through a particle swarm optimization algorithm; inputting the multi-dimensional feature matrix into an LSTM neural network, dividing equipment state grades according to equipment health indexes, outputting an operation state evaluation result of the GIS switch equipment, and performing graded early warning; according to the method, the recognition accuracy of the operation state of the GIS switch equipment is effectively improved.

The invention discloses an aluminum profile surface defect detection method and a storage medium, and the method comprises the following steps: S1, image preprocessing and data set construction; s2, improving the design of a YOLOv8 network structure; s3, model training and deployment; the image quality is optimized through wavelet transform denoising and histogram equalization, and the feature identification degree of the tiny defects is enhanced; a C2fFaster DCNv3 module and an ADFR module are adopted to enhance the multi-scale feature extraction and fusion capability; the newly added P2 layer detection head is specially adapted to tiny defects; the focus degree of a difficult sample is increased through weighting of a Focaler-IoU loss function, and the problems of missing detection and false detection of tiny defects are effectively solved; the Faster Block branch reduces the calculated amount, the TensorRT reasoning accelerates and optimizes the deployment efficiency, the real-time detection requirement of industrial production is met on the premise that the detection precision is guaranteed, and the dual purposes of high precision and high speed are achieved.

The invention discloses a method and system for monitoring the start-stop state of a conveying belt based on a traditional visual algorithm, and belongs to the technical field of traditional visual algorithms, and the method comprises the steps: installing an industrial camera at a proper position above the conveying belt; after receiving the original image acquired by the industrial camera, the monitoring host carries out graying processing on the image and carries out de-noising processing on the grayscale image; performing histogram equalization processing on the denoised image to enhance the contrast of the image; selecting a plurality of feature points in the belt area, and recording coordinates and feature descriptors of each feature point; for every two adjacent frames of images, matching the extracted feature points by using a feature point matching algorithm, and calculating an optical flow vector of each pixel point between the adjacent frames by using a feature descriptor-based matching algorithm and an optical flow method; and outputting a state and giving an alarm. Manual intervention is greatly reduced, accuracy is high, cost is low, adaptability is high, and implementation and maintenance are easy.

The application discloses a fruit calyx / fruit stalk and surface defect high-precision identification method for enhancing polarization characteristic difference. The method comprises the following steps: calculating a polarization degree image and a horizontal polarization enhancement image of a calyx / fruit stalk and a defect area by collecting four-angle polarization images; using median filtering and histogram equalization to pre-process the polarization degree image and the horizontal polarization enhancement image, then constructing a polarization representation parameter fusion algorithm, and outputting a polarization parameter fusion image; and establishing a multi-modal model for calyx / fruit stalk and defect area detection based on the polarization parameter fusion image and a visible light image. The application breaks through the limitation of high error rate of traditional visible light image detection of calyx / fruit stalk and defects, and uses a polarization representation fusion image and a visible light image to construct a multi-modal calyx / fruit stalk and defect detection method. Meanwhile, the method is simple in device and low in cost, and can be applied to an actual fruit production grading production line.

The application discloses an infrared image enhancement method, system, device and storage medium based on a wavelet threshold, which comprises the following steps: performing multi-scale discrete wavelet transform on an input original infrared image to separate low-frequency components and high-frequency components; the low-frequency components bear overall outlines and illumination distribution information of the image, and the high-frequency components contain edge, texture details and noise information of the image; performing improved adaptive wavelet threshold denoising processing on the high-frequency components to obtain denoised high-frequency components; performing layered enhancement processing based on weighted guided filtering and multi-scale Retinex on the low-frequency components to obtain enhanced low-frequency components; performing wavelet inverse transform reconstruction on the enhanced low-frequency components and the denoised high-frequency components to obtain a preliminary enhanced image; performing size upsampling on the preliminary enhanced image, and applying adaptive contrast restriction histogram equalization processing to output a final enhanced infrared image. The method can effectively suppress noise, retain details, adaptively enhance low-resolution infrared images, and is computationally efficient.

The invention relates to the technical field of overwater target detection, in particular to an overwater target anchor-frame-free detection method and system for inhibiting water surface reflection interference, which utilizes histogram equalization and normalization operation to improve image quality and contrast ratio, extracts reflection features through a trained and optimized convolutional neural network, and improves the image quality and contrast ratio. Then, according to the extracted inverted image features, a self-adaptive inverted image suppression strategy is adopted, an inverted image area is judged and weakened by calculating the similarity between inverted images and target area feature vectors, inverted image interference is effectively suppressed, then a Center Net anchor-frame-free target detection algorithm is adopted, the center point, the width, the height and the category of a target are directly predicted, and the target detection accuracy is improved. And the detection accuracy is improved by combining the characteristics of the water target, and finally, the detection result is post-processed to remove a redundant detection frame. The method can effectively inhibit water surface inverted image interference, avoids the complexity of anchor frame design, is high in adaptability to a complex water surface environment, and can improve the precision, reliability and efficiency of target detection.

The invention provides a fan blade image adaptive parallel backlight enhancement method, and relates to the technical field of fan blade inspection, and the technical key points are that the method comprises the following steps: image preprocessing; performing color space conversion and brightness normalization on the preprocessed RGB image; constructing a gamma correction-histogram equalization double-path parallel adaptive image enhancement model; a dynamic weight coefficient is designed, and the histogram equalization image and the gamma correction image are dynamically fused; and based on the dynamic parameters, establishing a five-dimensional parameter space, and obtaining a backlight enhancement image under the optimal parameter combination by using a network search method. Through YUV space brightness channel separation and double-channel parallel enhancement, while a gamma correction branch recovers details of a dark area, a histogram equalization branch is used to suppress an overexposure area so as to realize dynamic balance of the dark area and a bright area in a backlight image; realizing collaborative optimization of global brightness balance and local detail enhancement by adopting a dynamic weight fusion strategy; and a self-adaptive optimization mechanism is realized through a grid search method.

The application discloses a kind of AI multi-modal feature fusion-based power battery thermal runaway precision early warning system, and multiple-source data acquisition module obtains battery electric parameter and thermal distribution data, and wavelet packet decomposition and adaptive platform histogram equalization are used to pre-process;AI feature fusion module uses wavelet packet decomposition and improved RepLKNet network cascade to extract multi-channel time-frequency feature, uses bidirectional gate recurrent unit and multi-head global attention mechanism to extract time series feature, realizes adaptive cross-modal fusion by multi-scale discrete wavelet transform decomposition and mutual information weighted reconstruction, and intelligent decision early warning module uses reinforcement learning framework to dynamically adjust multi-classifier decision weight, and adaptively adjusts early warning threshold;Cloud iterative optimization module uses momentum federal average algorithm to carry out efficient model iteration and OTA push, and the application realizes the comprehensive extraction and depth fusion of multidimensional fault feature, the intelligent decision of whole life cycle adaptation and efficient cloud collaborative iteration.

The invention provides an intelligent cleaning method and device for a gravity concentration shaking table based on machine vision, and belongs to the technical field of intelligent maintenance of mineral processing equipment. Performing flatness detection on the acquired image data, dividing the image data based on a flatness detection result, and enhancing the divided image data by adopting histogram equalization; performing feature extraction on the enhanced image data based on a pre-trained MobileNetV2 network model to generate a binary stain mask, and generating a dirt distribution parameter in combination with a dirt coverage rate and a thickness estimation value; therefore, intelligent cleaning decision parameters are dynamically adjusted. By collecting and analyzing table surface image data of the shaking table and combining flatness detection and histogram equalization enhancement, the precision and adaptability of image processing are remarkably improved; a binary stain mask is generated based on a MobileNetV2 network model, and a dirt coverage rate and a thickness estimation value are fused, so that accurate quantization of dirt distribution parameters is realized; the intelligent cleaning decision parameters are dynamically adjusted, the cleaning efficiency is optimized, and resource consumption is reduced.

The application provides a CLAHE image blur enhancement method based on a mixed membership function. The method improves the single membership function in the existing CLAHE method based on the fuzzy theory into a mixed membership function, dynamically fuses the exponential and linear membership functions in the threshold clipping process, realizes the differential processing of the flat area and the area rich in details, and thus adaptively adjusts the clipping threshold, so as to solve the problems of the insufficient histogram equalization capacity, the easy amplification of noise and the poor edge retention effect of the prior art. Compared with the traditional method, the image enhancement quality of the method is significantly improved under multiple scenes.

According to the face image processing method and device, the storage medium and the computer equipment provided by the invention, after the to-be-processed face image is acquired, the face region in the face image is firstly determined, and the key points of the five sense organs are marked, so that a space coordinate basis of the key region is provided for subsequent processing; and then, a brightness channel image is separated from the face region, and the scene category of the face image is judged by quantifying the illumination distribution characteristics of the face region, so that a decision basis is provided for subsequent processing. For example, in a dark scene, five-sense-organ brightness hierarchical correction is performed to maintain skin pore-level textures while brightening; performing dynamic range compression in a strong backlight scene to balance light and shade transitions; limited histogram equalization is performed in a normal illumination scene to prevent over-sharpening or noisy explicit of the image. In addition, noise reduction processing, detail restoration and feature enhancement are sequentially carried out on the image after enhancement processing, and the finally generated image can greatly improve the recognition accuracy and the living body detection passing rate.

This invention discloses a method for estimating sea state levels based on visual information. The method includes acquiring image and video data of a target sea area through an image acquisition module; using a combination of median filtering and Gaussian filtering for noise reduction, and histogram equalization to enhance data contrast; performing image geometric correction using radial distortion correction and tangential distortion correction models; extracting wave texture features, motion features, and wave height features from the preprocessed data; inputting the extracted features into a trained sea state level classification model for training, and outputting the sea state level estimation result. During training, multi-model fusion and dynamic model updating methods are used to improve the accuracy and reliability of the estimation. This invention uses a camera to collect wave information within a certain distance and range, and automatically estimates the sea state level based on this information, thus solving the problems of high cost, limited measurement range, significant environmental influence, and low accuracy in existing sea state level estimation methods.

The invention relates to the technical field of sliding table testing, in particular to a three-axis sliding table automatic testing system and method, electronic equipment and a storage medium. The system has the advantages that the Python script is integrated with the mechanical sliding table control module, the image recognition module and the report generation module, full-closed-loop automatic testing from button pressing to result analysis is achieved, and manual operation errors are eliminated. Compared with a traditional manual method, the test efficiency is improved by more than 80%, the single test period is shortened from 8 seconds to 1.5 seconds, the method is particularly suitable for a ten thousand secondary service life test scene, the labor cost is remarkably reduced, full-process automation and test efficiency improvement are achieved, and the consistency of pressing actions is ensured by combining a dynamic G code generation technology with pressure sensor feedback; through the multi-scale SSIM algorithm and histogram equalization processing, the identification accuracy of 98% is still kept when the illumination change is + / -500 lux, the problem of misjudgment caused by ambient light interference in a traditional method is solved, and the high-precision control and environment anti-interference capability is improved.

This application provides a multi-scale hybrid DR image enhancement method, apparatus, electronic device, and storage medium. The method includes: acquiring a target image to be identified; dividing the target image into N blocks according to N different block scales to obtain a first block image corresponding to each block scale; for each block scale, performing histogram equalization processing on each first block image corresponding to that block scale according to its corresponding contrast constraint to obtain a corresponding second block image; for each block scale, performing edge smoothing processing on each second block image corresponding to that block scale to obtain a corresponding third block image; and fusing all third block images corresponding to the N different block scales to obtain an enhanced image. This application solves the problem in related technologies where single local histogram equalization is difficult to simultaneously capture image details at different scales.

The invention discloses a bubble three-dimensional reconstruction method based on a point cloud technology, and relates to the technical field of fluid mechanics and computer vision. The invention provides a bubble three-dimensional reconstruction method based on a point cloud technology in order to solve the problem that in the prior art, a dynamic bubble three-dimensional structure is difficult to accurately and rapidly reconstruct in a complex geometric environment, and consequently the interface area concentration and flow characteristic analysis precision is insufficient. According to the method, bubble two-dimensional images are synchronously collected through a multi-angle high-speed camera, after histogram equalization and clear contour extraction, point cloud data are generated layer by layer in the bubble height direction, and multi-view point cloud registration is carried out through an iterative nearest point algorithm. And then a continuous closed three-dimensional model is generated, and model optimization is realized in combination with Laplacian smoothing and normal filtering. According to the method, the real form of the bubbles can be recovered with high precision, and reliable technical support is provided for gas-liquid two-phase flow interface area concentration calculation and dynamic behavior analysis in complex environments such as nuclear reactor rod bundle sub-channels.