4609 results about "Image enhancement" patented technology

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

The invention discloses a manipulator grabbing method based on deep learning target detection and image segmentation, and relates to the technical field of artificial intelligence and robotics.The manipulator grabbing method comprises the following steps that a scene image to be processed is collected, the image quality is improved through the multi-light-source fusion image enhancement technology, and recognition errors caused by uneven illumination are reduced; and inputting the enhanced image to a pre-trained deep learning model, executing a target detection task, and outputting an initial bounding box and a category label of the target object. According to the method, through multi-light-source image enhancement and high-precision image segmentation, the accuracy of target recognition and contour extraction is remarkably improved, and the capture failure rate caused by image misjudgment is reduced. And meanwhile, geometric consistency verification and a multi-factor grabbing scoring mechanism are introduced, dynamic screening and collision pre-detection are conducted on the paths, the grabbing stability and safety of the mechanical arm in the complex environment are effectively guaranteed, and the intelligence and robustness of the whole system are remarkably improved.

The invention discloses a weldment welding seam automatic detection method and device based on machine vision, and relates to the technical field of machine vision intelligent detection. The weldment welding seam automatic detection method and device based on machine vision comprises the steps that S1, surface images and forming feature data of a weldment are collected and preprocessed to construct a standardized image feature data set; s2, the boundary clearness of the weld joint is evaluated by combining the edge strength and the contour coherence, and the main contour extraction range is dynamically adjusted; s3, analyzing abnormal focusing characteristics of the candidate area, and adjusting a defect labeling range and a detection priority; and S4, integrating the boundary definition and the abnormal focusing features, analyzing the structure abnormality, and dynamically controlling and verifying a resource allocation strategy. The problems that in the weldment detection process, obvious light reflection and texture blurring phenomena exist in a heat affected area at a weld joint, a traditional image enhancement and edge extraction algorithm is difficult to stably recognize microdefects, and the credibility of a detection result is reduced are solved.

The invention discloses an insulator defect detecting and positioning method and system based on cross-scale feature fusion, and belongs to the field of intelligent routing inspection of power distribution network lines, and the method comprises the following steps: S1, obtaining image data containing a plurality of insulator defects of the power distribution network lines in real time, and carrying out the preprocessing of the image data; s2, image enhancement; s3, fusing the enhancement results under multiple scales obtained in the step S2 by adopting a pyramid fusion strategy to obtain a fused image; s4, inputting the fused image into a pre-trained cross-scale feature fused defect detection model, and outputting defect features; and S5, based on the detection time and the real-time speed of the defect features, considering the delay time, and predicting the position coordinates of the defect features. By the adoption of the insulator defect detection positioning method and system based on cross-scale feature fusion, high precision, high robustness and self-adaptive processing of insulator defect detection are achieved, and the method and system have important significance on intelligent detection and maintenance of insulator defects of a power distribution network line.

The invention discloses a face image enhancement and recognition method in a low-light environment, and relates to the technical field of face recognition. Firstly, multi-scale Retinex enhancement is carried out on a low-illumination face image, brightness and details are improved, then LBP and HOG features of the enhanced image are extracted, weight fusion is dynamically adjusted according to illumination, then space and channel weighted optimization is carried out on the fused features by using a residual attention module, finally, optimized features are input into a pre-training model to extract vectors, and finally, the pre-training model is used for pre-training. And through cosine similarity matching identification, face processing and identity determination under low illumination are completed. According to the method, the problem of low-illumination face recognition is solved, the image quality is improved through multi-scale enhancement, feature extraction is enhanced through dynamic fusion and an attention mechanism, and the recognition accuracy and stability are improved; in addition, living body detection is added to guarantee safety, and the method is suitable for multiple scenes such as security and protection, finance and the like.

The invention discloses a super-resolution image enhancement system and method based on a variational mode decomposition algorithm. The system comprises an adaptive decomposition module, an enhancement processing module, a fusion module and an optimization module. The adaptive decomposition module receives low-resolution image signals, generates modal component signals containing different frequency band characteristics, and outputs modal quantity parameter signals according to image frequency domain energy distribution. The enhancement processing module comprises a high-frequency enhancement unit and a low-frequency reconstruction unit, and generates a high-frequency enhancement signal and a low-frequency reconstruction signal. And the fusion module receives the modal quantity parameter signal, the high-frequency enhanced signal and the low-frequency reconstructed signal, and performs spatial adaptive weighted fusion on the high-frequency signal and the low-frequency signal through a dynamic weight coefficient to generate an initial high-resolution signal. And the optimization module carries out adaptive nonlinear filtering processing on the initial high-resolution signal. The super-resolution image enhancement system based on the variational mode decomposition algorithm can solve the problem that the prior art is difficult to adapt to a complex image structure.

The invention discloses a financial data intelligent entry and verification method, which comprises the following steps: acquiring an original document image, and processing the original document image by adopting an image enhancement algorithm to obtain an optimized document image; extracting a structured field from the optimized document image, determining field coordinates, and generating a structured data table; performing semantic analysis on the non-standardized text in the structured data table to generate semantic vector representation; identifying missing fields according to the semantic vector representation, querying a historical database, and generating preliminary filling data; comparing the preliminary filling data with historical data through a cross validation mechanism to obtain a validated field value; processing the verified field value by adopting a classification model, and determining a document type; arranging the verified field value according to the document type, and generating a standardized data record; key indexes are extracted from the standardized data records, the index logic relation is verified, and corrected data are obtained.

The invention discloses a bridge structure health monitoring data anomaly detection method based on deep learning, particularly relates to the technical field of structure health monitoring, and is used for solving the problems of high environmental interference sensitivity and insufficient cross-modal data fusion capability caused by image enhancement and feature extraction process splitting in the existing method. A cross-domain feature mapping relation is generated through combined training of dynamic image enhancement and a deep learning model, and collaborative optimization of enhancement parameters and feature space is achieved; time-frequency resonance parameters of visual images and acoustic emission signals are fused based on cross-modal convolution, and damage feature space distribution is corrected in combination with an attention mechanism; analyzing and quantifying the structural difference of the cross-domain features by using topology persistence coherence, and iteratively optimizing the feature mapping network through an optimal transmission theory; and finally, a multi-level feature template matching and self-adaptive threshold judgment mechanism is adopted to output an abnormal detection result, so that the robustness and generalization ability of bridge structure health detection in a complex environment are remarkably improved.

The invention discloses a low-illumination image enhancement method based on a Retinex theory, and belongs to the field of low-illumination image enhancement. According to the method, on the basis of the Retinex theory, three aspects of innovations are developed: a double-branch decomposition network DecomNet is established, and precise decoupling of illumination and reflection components is realized; a diffusion model is introduced to denoise reflection components, and noise suppression and detail retention are both considered by means of a self-constraint consistency loss function; reLumenNet is constructed, an LIT module and a CBAM mechanism are fused, and illumination is regulated and controlled in a self-adaptive mode. By means of a series of innovative designs, the low-illumination image enhancement effect is greatly improved, and the problems of brightness distortion, noise interference, detail missing and the like are effectively solved. The method has high practical value in the scenes of night monitoring, automatic driving and the like, and a novel low-illumination image enhancement method is provided on the technical level.

The invention relates to the technical field of image data processing, in particular to a cable surface defect detection method and system based on machine vision. The method comprises the following steps: acquiring a surface image of a cable, and converting the surface image into a grayscale image; determining the local complexity of each pixel point; obtaining a plurality of areas of the grayscale image, performing complexity determination, and dividing each area to obtain a plurality of windows of each area; determining a contrast limit threshold value of each window; performing image enhancement by using a CLAHE algorithm to obtain an enhanced grayscale image; and carrying out cable surface defect detection on the enhanced grayscale image by using a defect detection algorithm. According to the method, the CLAHE parameters are adaptively adjusted based on local complexity, the window size and the contrast threshold are dynamically determined in combination with gray and gradient information, discontinuity is corrected and eliminated through boundary similarity, and the quality and reliability of a cable defect detection image are improved.

The invention discloses a low-light image enhancement method based on multi-scale frequency domain guidance and a double-branch attention mechanism. According to the method, a low-light image and a normal image corresponding to the low-light image serve as input, and illumination mapping and illumination features are extracted through a layer decomposition network; a reflection image mapping relation is learned in combination with a damage recovery network, and a preliminary enhanced image is generated; a learnable Fourier transform module is provided to realize frequency domain feature extraction, and low-frequency illumination information and high-frequency details are effectively separated; a dynamic double-branch attention mechanism is put forward, spatial domain and frequency domain features are fused, and collaborative modeling of structure and illumination perception is achieved; a multi-scale feature module is proposed to combine channel rearrangement and residual fusion to optimize feature expression; a U-Net framework is combined with a double-branch large kernel activation attention mechanism to complete image reconstruction, cross-scale feature fusion and detail recovery are achieved, and finally an enhanced image is output. According to the method, the problems of uneven illumination, detail loss, noise interference and the like in the low-light image can be effectively solved.

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

The invention discloses a light guide plate defect detection method and system based on a neural network, and particularly relates to the technical field of machine vision detection, and the method comprises the following steps: aiming at the problem of image instability of a light guide plate in a dynamic transmission or rotation process, continuously collecting an image sequence and extracting time domain features; and performing interference judgment in combination with the inter-frame consistency prediction coefficient and a first threshold to realize accurate identification of the abnormal image frame. For an abnormal image frame, further correcting the recognition credibility of the abnormal image frame by adopting a confidence adjustment and fusion mode, and meanwhile, introducing a frequency domain transformation and image enhancement strategy to compensate detail loss caused by motion blur; according to the method, inter-frame consistency analysis, confidence fusion regulation and control and frequency domain fuzzy recognition and compensation mechanisms are introduced, abnormal judgment and image quality restoration of the light guide plate image in the dynamic scene are realized, the recognition accuracy and stability of the neural network model on the defect type, position and confidence are improved, and the false detection and omission ratio is effectively reduced.

The invention relates to the technical field of road engineering detection, in particular to a highway pavement crack image intelligent detection system and method, and the system comprises an image acquisition module, a deep learning module, an image enhancement module, a crack measurement and calculation module, a crack development trend prediction module and an information visualization module. The core innovation of the invention lies in that a differential geometry theory is introduced to construct a crack development trend prediction module, and the module comprises a crack characterization model based on a differential manifold, a multi-scale crack evolution tensor field analysis model and a nonlinear space-time crack development prediction model. A pavement is regarded as a two-dimensional differential manifold, cracks are represented as singular curves on the manifold, a multi-scale tensor field analysis technology and a non-linear kinetic equation are combined, accurate prediction of the future development trend of the cracks is achieved, the system supports multiple image acquisition modes, transverse, longitudinal and net cracks can be accurately detected, and the detection precision is high. The method adapts to complex illumination and background conditions, and predicts the expansion rate and severity change of the crack.

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 slope disease identification method and device based on an unmanned aerial vehicle and a machine vision technology, and the method comprises the steps: carrying out the multi-angle image collection of a slope region on a preset flight path through an unmanned aerial vehicle carrying a high-resolution camera; preprocessing the image, and performing feature extraction and image enhancement; recognizing and classifying disease features by combining an improved YOLOv8-seg instance segmentation algorithm, wherein slope diseases such as cracks, landslides, collapse and the like are covered; quantifying the risk level of the slope disease by using a rule model or a multi-factor analysis method based on the identification result; and carrying out dynamic change prediction on the acquired disease time sequence image by adopting a long-short term memory network (LSTM), and predicting the development trend and the potential instability time of the disease. On the basis of image data collected by the unmanned aerial vehicle, real-time monitoring and dynamic analysis are carried out on the slope diseases, potential hidden danger areas are found in time, disease features can be accurately recognized, and subjective errors possibly caused by manual judgment in a traditional method are avoided.

The invention discloses a multi-mode CNN / Transform image defect diagnosis tracking decision-making method, and belongs to the field of power electronic industry detection. According to the method, defect position multi-modal data of different batches, process stages and equipment are collected, after space-time alignment and image enhancement preprocessing and alignment are conducted, features are extracted through position coding, a CNN shallow network and ResNet-18, the features are fused into multi-modal feature vectors in combination with weights, a detection model is obtained through CNN / Transform model training, defect diagnosis and tracking decision making are achieved, and the defect diagnosis and tracking decision making efficiency is improved. The problems of precise detection, positioning, diagnosis tracking and decision-making of internal tiny component changes and structural defects of electronic products and local and overall multi-mode defects of single electric power are solved. The detection accuracy is improved by 20%, the diagnosis accuracy is more than 98.5%, the defect tracking error is less than 5%, the decision-making efficiency is improved by 40%, and the defect prediction capability is improved by 22%.

The invention relates to the field of target detection, in particular to a UMFNet-YOLO-based joint detection algorithm under a low-light condition, which adopts a multi-scale decomposition and high-frequency-low-frequency feature collaborative enhancement strategy, separates image details from global features through high / low-pass filtering, realizes multi-resolution feature complementation in combination with dynamic weight distribution, and improves the detection accuracy. Target contour and texture information degraded in severe weather can be effectively recovered; a parallel channel compression and spatial semantic enhancement module is designed, rain and fog noise interference is suppressed through cross-dimensional adaptive feature screening, and significance expression of a key target is enhanced; an image enhancement network UMFNet is cascaded with YOLOv8, and strong correlation between feature expression and a detection task is ensured by synchronously optimizing an enhancement module and detecting network parameters through back propagation. Compared with a baseline YOLOv8 detector, the method has the advantages that the average precision (mAP50) is obviously improved, and the method is obviously superior to a traditional enhancement-detection separation scheme. The method can be applied to robust target detection scenes in complex environments such as automatic driving and security monitoring.

The invention relates to the technical field of target detection, and discloses a pet target detection method and device and a camera, and the method comprises the steps: carrying out the motion triggering collection and image enhancement preprocessing of a front end region of a feeder, and obtaining an enhanced image frame sequence; performing feature extraction of dynamic receptive field adjustment on the enhanced image frame sequence to obtain pet feature descriptors and position information; behavior time sequence feature analysis is executed, and pet behavior sequence feature vectors are obtained; constructing a state transition diagram according to the pet behavior sequence feature vector, and performing time sequence consistency analysis to obtain a pet state judgment result; power management and decision execution are carried out on the feeder based on the pet state judgment result, feeding control under the low-power-consumption condition is achieved, behavior misjudgment caused by posture fluctuation is effectively avoided, the behavior recognition accuracy is improved, the accurate feeding control problem in a multi-pet family is solved, and the user experience is improved.

The invention belongs to the technical field of image recognition, and provides a target tracking method and system based on AI vision, and the method comprises the following steps: collecting original video frames; environment adaptive image enhancement; performing multi-target detection and multi-modal feature extraction; estimating local optical flow motion; performing multi-target trajectory association; carrying out shielding processing and re-identification; outputting a track and analyzing a result; according to the method, a physical-deep learning cascade defogging model is set, light / dense fog processing paths are dynamically switched through a dark channel mean value, atmospheric scattering physical prior and U-Net residual error correction are fused, an environment self-adaptive sensing architecture is provided, the failure bottleneck of a traditional single model under sudden change fog concentration is broken through, and the real-time performance of the system is improved. According to the method, an apparent-motion-geometry ternary coupling trajectory cognition system is constructed, a dynamic cost matrix and a feature cache pool are designed, the ID switching problem caused by similar target aggregation and long-time shielding is solved, and the accuracy of target tracking in the shielding environment is improved.

The invention discloses an intelligent duck shed monitoring method and system based on image recognition, and the method comprises the steps: obtaining environment monitoring data from an environment monitoring device, obtaining original image data of a breeding region from an image collection device, and processing the original image data through an image enhancement algorithm, thereby obtaining a preprocessed image data set; according to weight parameters output by the duck group behavior and environment correlation model, performing weighted fusion on the environment monitoring data and the preprocessed image data set to generate a multi-modal feature data set; if the group behavior index in the multi-modal feature data set deviates from a preset threshold range, extracting a spatial-temporal feature map through a convolutional neural network, and generating individual behavior trajectory data in combination with a multi-target tracking algorithm; and inputting the individual behavior trajectory data into the lightweight neural network model, and outputting a health state classification result. According to the intelligent duck breeding system, accurate monitoring and intelligent adjustment of the duck breeding environment are achieved, and the breeding efficiency and the duck group health level are effectively improved.

The invention discloses an improved underwater image enhancement method. The method comprises the following steps of lightweight downsampling and feature extraction; performing multi-stage attention enhancement and feature optimization; multi-path feature reconstruction is carried out; and performing multi-stage output fusion and image restoration. According to the method, a UNet + + model is introduced into the field of underwater image enhancement for the first time, standard convolution of a down-sampling layer is replaced by lightweight convolution, a three-stage attention mechanism of local noise suppression, space-channel collaborative optimization and global color cast compensation is cascaded after each layer of lightweight convolution, and a multi-level enhancement result is generated. And finally fusing and outputting through a weighted average strategy. And through lightweight convolution replacement, the model parameter quantity and the calculation complexity are remarkably reduced, and the calculation efficiency is improved. Through a three-stage attention mechanism, typical degradation problems of suspended particle noise, edge blur, color distortion and the like of the underwater image are accurately repaired. According to the method, the common artifact problems of light spots and the like and the excessive smoothness problem in the existing method are solved.

The invention relates to the technical field of industrial machine vision detection and image processing, in particular to an automobile part production mold surface smoothness detection system based on image enhancement, which comprises a data acquisition module used for acquiring an original grayscale image of the surface of an automobile part mold; performing low-pass filtering processing on the original grayscale image to eliminate imaging thermal noise; the manifold reconstruction module is used for constructing a structure tensor field; reversely deducing a pseudo-curvature field of the mold surface; the adaptive enhancement module is used for generating a corrected image; constructing a texture orthotropic diffusion model; generating a texture reconstruction reference image; the surface metering module is used for calculating the difference between the corrected image and the texture reconstruction reference image and generating a defect saliency image; calculating the surface roughness value of the mold surface; according to the method, the problem that design textures and abnormal scratches are difficult to distinguish in the prior art is effectively solved, and the technical bottleneck that micro defects are easily missed in a complex geometric structure in traditional visual detection is overcome.

The invention relates to the technical field of image processing, and discloses a multispectral fusion night low-illumination image enhancement and shielding compensation method, which comprises the following steps of: cooperatively acquiring multi-modal data through a visible light camera, an infrared sensor, a thermal imaging sensor and a millimeter wave radar; comprising a low-illumination basic image, dark light texture details, target temperature distribution and contour and motion information of an object behind the shelter; fusing visible light and infrared textures by adopting a multi-scale transformation algorithm to generate a transition fusion image, and performing illumination compensation based on temperature distribution; predicting texture details of the occlusion area through a u-Het deep learning network in combination with detection data of the millimeter wave radar; and filling the missing texture according to the shielding contour, and generating a final target image through edge optimization and illumination smoothing technologies. According to the invention, the definition of a night low-illumination image can be improved.

The invention discloses a bronze ware ornamentation and pattern digital restoration method based on an image enhancement technology, and relates to the technical field of image restoration, and the method comprises the steps: building a space mapping matrix; extracting multi-modal data features by using the surface state of the topological insulator, and registering a joint data volume; forming a super-resolution image through super-resolution reconstruction; obtaining a material degradation coefficient by using a wavelet finite element method and a graph neural network; generating an adversarial network by utilizing physical constraints, and generating an embarrassment repairing result; a material sensing three-dimensional model is constructed by adopting a wavelet packet decomposition and neural radiation field fusion technology, texture mapping is dynamically adjusted based on a graphene Moire effect, and virtual-real fusion is performed through holographic waveguide AR; by combining advanced technologies such as a metamaterial lens, micro-distance laser scanning, a topological insulator film, a graphene heterojunction and a nerve radiation field, high-precision three-dimensional digital restoration and repair of bronze cultural relics are realized, and immersive augmented reality display experience is provided.

The invention provides a small target identification method and system based on YOLOv5, and the method comprises the steps: collecting a multi-scale image in a target range, and forming an original data set; performing image enhancement on the original data set through superpixel segmentation and adversarial enhancement operation to obtain an enhanced data set; performing multi-scale frequency domain aliasing enhancement on the enhanced data set through frequency domain decomposition and frequency band exchange operation to obtain a to-be-detected data set; improving the YOLOv5 model to obtain a small target recognition model; and performing small target identification on the to-be-detected data set through the small target identification model to obtain an identification result. According to the method, small target recognition is realized through the dynamic feature pyramid and the double-path detection head in combination with cross-level kernel sharing and a space-frequency double-domain attention mechanism, the problem of detail loss caused by a traditional static feature pyramid is solved, the recognition precision is improved, and the false detection rate is reduced.

The invention discloses a weak light image enhancement method fusing noise adaptive diffusion and illumination perception, and the method comprises the steps: firstly designing a reflection-illumination decomposition module RID based on a Retinex theory, and extracting a reflectivity component and an illumination component of an input weak light image; then designing an ADRD (adaptive diffusion reflection denoising) module, performing denoising enhancement on the reflectivity component, and outputting an enhanced reflectivity image; introducing a noise estimation network, and dynamically adjusting the de-noising amplitude of the diffusion model according to the noise intensity of the region in the reflectivity graph; designing an illumination perception enhancement module IEM, fusing an attention mechanism and a local enhancement block, enhancing the illumination image, and outputting an enhanced illumination component; and building a complete neural network framework, carrying out joint training on the provided module, finally testing the stored neural network model, and outputting an enhanced image. According to the method, the problems of detail loss, noise residue and uneven illumination of the enhanced image in the prior art are solved.

The invention discloses an image enhancement method for brightness gain self-adaptive regulation and control under low illumination, and relates to the technical field of image data processing. The image enhancement method for brightness gain adaptive regulation and control under low illumination comprises the following steps: S1, acquiring original image data and auxiliary processing data under a low illumination environment for preprocessing, and constructing an image processing database after storing the original image data and the auxiliary processing data; s2, performing illumination distribution optimization and brightness correction operation through brightness deviation analysis, and outputting an illumination enhancement component; s3, performing de-noising and detail enhancement processing according to a noise color deviation result, and outputting a corrected reflectivity component; s4, through image quality enhancement analysis, using a loss function to guide a double U-Net architecture to carry out detail reconstruction and color optimization on the image; and S5, based on the quality feedback and the difficult sample, adjusting processing parameters and driving algorithm closed-loop optimization. The problems of insufficient image brightness, noise interference, color distortion and detail missing multiple degradation in a low-light environment are solved.

The invention discloses a multi-wave detection and imaging system for fish schools, which relates to the field of detection and imaging of fish schools and comprises a metamaterial acoustic emission module, a self-adaptive signal receiving module, a three-dimensional point cloud generation module, an image enhancement and generation module and an intelligent decision control module. According to the multi-wave detection and imaging system for the fish school, clustering parameters can be dynamically adjusted based on the real-time fish school density, the neighborhood radius is automatically expanded in a high-density area to avoid segmentation errors, and the core point judgment threshold is reduced in a low-density area to reduce missing detection; an acoustic attenuation model is constructed in combination with water body environment parameters, emission parameters are calibrated in real time, images can be enhanced, space and time dimension features are fused, the image resolution and definition are improved, accurate recognition of multiple fish species is achieved, the fish species can be better distinguished, meanwhile, a generative network is adopted to directly convert acoustic data into visual images, and the recognition accuracy is improved. Therefore, the generated image can reflect the actual distribution condition of the fish school more truly and accurately.

A system and method are disclosed for low-light image enhancement using hierarchical adaptive wavelet decomposition with cross-scale feature fusion. The system analyzes a raw input image to determine image characteristics and preprocessing parameters. A hierarchical adaptive wavelet decomposition process creates a variable-depth decomposition tree comprising frequency domain nodes, with decomposition depth determined by local image complexity. Cross-scale feature fusion implements attention mechanisms between nodes at different decomposition levels, enabling bidirectional information flow across scales. A dynamic network pool allocates specialized neural networks to process nodes based on their frequency characteristics, with weight sharing between similar nodes for efficiency. An adaptive reconstruction engine traverses the decomposition tree using learned filters and multi-scale residual learning to produce an enhanced image. The hierarchical approach enables superior low-light image enhancement by allocating computational resources based on content complexity, achieving better quality than fixed decomposition methods while maintaining compatibility with existing image signal processing pipelines.