107 results about "Non-local means" 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 provides an image recognition system and method based on deep learning, and the system comprises a self-adaptive optical collection module, a heterogeneous preprocessing pipeline, a hierarchical reconfigurable convolutional network, a multi-dimensional training optimization engine and a cross-modal verification output interface, aperture parameters are dynamically adjusted through deep reinforcement learning; the heterogeneous preprocessing pipeline comprises a quantum noise modeling non-local mean noise reduction unit, a double-discriminator generative adversarial network enhancement unit and a dynamic normalization unit; the hierarchical reconfigurable convolutional network adopts a staged feature distillation structure and comprises a separable convolution module, a mixed pooling layer and a three-dimensional attention fusion module. According to the invention, through a multi-modal data fusion and dynamic optimization mechanism, the image acquisition quality in a complex illumination and noise scene is improved, the adaptability of the model to different environments is enhanced, and all modules work cooperatively to realize an end-to-end efficient identification process.

The invention discloses a cross-modal eye fundus image generation method and system based on a generative adversarial network, relates to the technical field of medical image processing, and constructs an eye fundus focus perception and edge consistency generative adversarial network by taking a cyclic consistency generative adversarial network as a baseline. The core of the method is that a lesion perception mixed attention module is embedded in a bottleneck layer of a generator so as to strengthen the extraction capability of fine features of a lesion area; an edge information extraction module is designed, and key edge features are accurately extracted in combination with Roberts edge detection, wavelet transform and non-local mean denoising; and a joint loss function containing edge consistency loss is constructed, and the semantic consistency of a focus structure during cross-modal generation is ensured by minimizing the feature difference between the source image and the generated image. According to the method, the problems of disordered content, inconsistent structure and unstable training of the generated image in the prior art are effectively solved, and the simulation degree and clinical availability of the generated image are remarkably improved.

The invention relates to the technical field of computer vision, and discloses a jade defect intelligent detection method and system based on machine vision and deep learning, and the method comprises the following steps: S1, based on a high-resolution industrial camera and a laser three-dimensional scanner, adopting a multi-mode synchronous collection strategy, and rotating a jade sample through a precise motion control system, a jade surface high-resolution two-dimensional color image and high-precision three-dimensional point cloud data are respectively obtained, and a jade multi-mode original data set is generated. A high-resolution two-dimensional color image and high-precision three-dimensional point cloud data are integrated through a multi-modal synchronous acquisition strategy, and multi-dimensional feature expression under unified coordinates is constructed, so that the limitation of a single data source is effectively overcome; an image registration algorithm and a feature pyramid network are combined with a point cloud network to perform multi-modal feature fusion, complementarity of color texture and geometric morphology information is enhanced, and image quality is optimized based on adaptive histogram equalization and non-local mean filtering.

The invention relates to the technical field of multiphase flow parameter detection, and particularly discloses a vertical pipeline gas-liquid-solid dilute phase annular flow solid content image detection method, which comprises the following steps: S1, carrying out multi-view image acquisition; s2, preprocessing the acquired image; s21, dynamic background differencing is carried out, and gas-liquid interface reflection noise is removed; s22, non-local mean filtering denoising is adopted, noise is removed, and detail information of the image is reserved; s23, carrying out local Otsu segmentation, and segmenting the particles from the background; s3, analyzing the preprocessed image; s31, performing spatial correlation verification, and eliminating multi-view repeated particles; s32, time correlation analysis is carried out, and a displacement vector of the particles is calculated; s33, correcting a displacement error by using an optical flow correction algorithm; and S4, carrying out particle data statistics, and calculating the solid content. According to the detection method, dynamic interference can be effectively resisted, a single-visual-angle blind area is eliminated, repeated particle counting is accurately eliminated, and the solid rate measurement precision is improved.

The invention provides a brain magnetic resonance image denoising method, system and device, a medium and a program product, and the method comprises the steps: obtaining a brain magnetic resonance image which comprises a plurality of stacked two-dimensional image slices with the same size, respectively carrying out denoising filtering processing and edge preserving filtering processing on each two-dimensional image slice based on a first filtering channel and a second filtering channel of a non-local mean value algorithm to obtain a corresponding first channel filtering image and a second channel filtering image; performing wavelet coefficient mixing processing on a first deviation correction image and a second deviation correction image obtained by performing Rice noise deviation correction based on the first channel filtering image and the second channel filtering image to obtain corresponding de-noised image slices; and obtaining a brain magnetic resonance de-noised image based on the stacking of the de-noised image slices corresponding to the two-dimensional image slices. According to the method, the brain imaging information can be fully reserved, the magnetic resonance image noise can be efficiently and accurately removed, and the image recovery quality and the recovery efficiency can be effectively improved.

The invention discloses a crayfish real-time counting method and system based on a dynamic visual sensor, and the method comprises the steps: sensor installation and adjustment, image collection, preprocessing, individual recognition, tracking and counting, and the image is preprocessed through adaptive threshold image segmentation and non-local mean denoising; identifying individuals by using a multi-feature fusion convolutional neural network and attitude compensation; kalman filtering is combined with a Hungary algorithm for tracking and coping with shielding, the system comprises a plurality of cooperation units, accurate counting is performed by applying partition and calibration models aiming at uneven distribution of crayfishes, and the system has the characteristics of parameter adaptive adjustment, ROI image acquisition optimization, fault detection and self-recovery, expandable counting model framework and the like. Counting accuracy and system stability are remarkably improved, and intelligent management of crayfish breeding is assisted.

The invention discloses an infrared image denoising method and system based on NSST decomposition, and the method comprises the steps: S1, decomposing a transformer infrared image containing noise based on an NSST algorithm, and outputting a high-frequency sub-band coefficient, a low-frequency sub-band coefficient and a high-frequency sub-band coefficient; s2, based on the low-frequency sub-band in the S1, filtering and denoising the low-frequency sub-band by adopting an improved non-local mean algorithm, and outputting the denoised low-frequency sub-band; s3, based on the high-frequency sub-band coefficients in the S1, introducing local entropies for different high-frequency sub-band coefficients, and outputting each high-frequency sub-band after the local entropies are denoised; and S4, based on the low-frequency sub-band in S2 and each high-frequency sub-band in S3, obtaining a denoised image by adopting NSST inverse transformation, and completing infrared image denoising. According to the method, the high-frequency sub-band coefficients containing different frequency noise are denoised by adopting the local entropy threshold method, noise filtering parameters do not need to be manually set, denoising threshold parameters are established according to entropy information of the high-frequency sub-band coefficients, and the method has good adaptability.

The invention discloses a stone surface flaw automatic identification method and system based on an intelligent algorithm, and belongs to the technical field of machine vision and stone processing. The invention provides an automatic detection scheme for solving the problems that in the prior art, manual stone slab defect detection is low in efficiency and high in subjectivity, and a traditional machine vision method is insufficient in detection precision under the conditions of complex stone slab textures and mixed noise. The method comprises the following steps: constructing an image acquisition system and carrying out camera calibration and image correction; the method comprises the following steps: preprocessing a stone plate image by adopting a denoising method combining median filtering and non-local mean (NLM) filtering, and extracting a stone plate contour by combining an improved Canny algorithm; and then, solving the maximum inscribed rectangle in the contour through a histogram area method, performing histogram equalization enhancement on the rectangular region, and finally, segmenting and identifying defects such as color spots and color lines by adopting a region splitting and merging algorithm combined with morphology.

The invention discloses a skeletal muscle ultrasonic image processing system and method for ultrasonic detection, and belongs to the technical field of medical ultrasonic image processing.The skeletal muscle ultrasonic image processing method comprises the steps that after a skeletal muscle ultrasonic image is subjected to non-local mean value dynamic filtering denoising, the contrast ratio of the skeletal muscle ultrasonic image is enhanced through nonlinear gray level transformation, and the skeletal muscle ultrasonic image is obtained; skeletal muscle areas are segmented by using the spatial continuity and similarity of skeletal muscles; on the basis of the segmented skeletal muscle region, carrying out binaryzation on a skeletal muscle ultrasonic image, calculating fractal dimensions by utilizing grid sliding statistics and weighting processing, constructing and smoothing a tensor model, calculating a main characteristic value and a characteristic vector, and positioning a skeletal muscle atrophy region through threshold judgment and clustering analysis; and performing labeling post-processing on the skeletal muscle ultrasonic image to generate a skeletal muscle ultrasonic image labeling report. The objective of the invention is to solve the technical problems of denoising, contrast enhancement, region segmentation, texture complexity quantification, atrophy region positioning, annotation report generation and the like of skeletal muscle ultrasonic images.

The invention relates to a multi-center mental image data enhancement method and system based on federal learning, and the method comprises the steps: receiving image data of a mental image of a patient and a modal identifier of the image data, converting the image data and the modal identifier into a capital form, and carrying out the matching; performing wavelet de-noising processing of the CT image on the CT mode, performing MRI image non-local mean de-noising processing on the MRI mode, and returning a de-noised image; carrying out parameter initialization, training sample number adaptive adjustment, noise level adaptive adjustment and model consistency adaptive adjustment on the federal guide intensifier, and training the federal guide intensifier; and carrying out image self-adaption on the CT image or the MRI image through the trained federal guiding enhancer based on the final parameter combination. An adaptive enhancement selector is utilized, conditional generative adversarial network pathological simulation and traditional deformation are fused, and samples reflecting real diversity are generated; designing a privacy-performance linkage mechanism; a lightweight secure communication layer is constructed, and inversion attacks are effectively defended while communication overhead is reduced.

The present invention discloses a method for extracting ship contours from SAR images based on a combined method of Faster R-CNN and a CV model, and relates to the technical field of satellite remote sensing image processing and application. The purpose of the present invention is to solve the problems of existing SAR image ship contour methods, such as limited applicable scenarios, poor extraction accuracy, and severe detail loss. The present invention can extract ship contours from large-scene SAR images by introducing a deep learning method. First, a Faster R-CNN network is used to perform transfer learning and target detection on the SSDD dataset to obtain the positioning coordinates of the ship target, and the small-scene ship area is sliced; then, the ship slice is subjected to fast non-local mean (FNLM) filtering processing, and finally, an improved CV model is used to iteratively generate the final ship contour, and the contour information is fused and displayed in the original image. The method for extracting ship contours from SAR images based on a combined method of Faster R-CNN and a CV model proposed by the present invention can achieve rapid and accurate extraction of ship target contours in SAR images of different scenes.

The invention relates to the technical field of industrial image quality detection, and discloses an image recognition-based toothbrush quality detection method, which comprises the following steps of: acquiring a to-be-detected image of a toothbrush, and performing non-local mean denoising and CLAHE contrast enhancement processing on the to-be-detected image to obtain a preprocessed image; pixel-level segmentation is carried out on the preprocessed image through an Attention-UNet + + model, and a toothbrush head area mask M1 and a toothbrush handle area mask M2 are generated; according to the detection method, the quality conditions of the toothbrush head and the toothbrush handle are independently and clearly known through regional evaluation, whether quality defects exist in the two regions or not and the severity of the defects can be judged respectively, and on the basis, the quality of the toothbrush head and the quality of the toothbrush handle can be judged by calculating a weighted total score Stotal and comparing the weighted total score Stotal with a dynamic threshold value T; according to the method, organic combination of independent evaluation and comprehensive decision is realized, and the combination mode not only considers quality characteristics of different areas of the toothbrush, but also comprehensively evaluates the quality of the toothbrush on the whole.

The invention discloses a hydroelectric generating set vibration signal de-noising method and system based on BPO-NLM, and relates to the technical field of signal de-noising processing, and the method comprises the following steps: based on a non-local mean filtering algorithm NLM, through a Bayesian parameter optimization algorithm BPO, optimizing the NLM; and carrying out noise reduction processing on the collected vibration signals of the hydroelectric generating set by utilizing the optimized NLM. The processing efficiency and accuracy of the hydroelectric generating set vibration signals are improved, and powerful support is provided for fault diagnosis and state monitoring of the hydroelectric generating set.

The invention discloses a flexible particle deformation detection method and system based on deep learning. The method and system are suitable for classification of two-dimensional particles in a Cryo-EM image. For the problems of low signal-to-noise ratio, complex particle deformation and the like in a Cryo-EM image, noise reduction processing is performed on an original image through methods of Topaz, low-pass filtering, non-local mean filtering and the like, so that the structural distinguishability is improved; then, carrying out binarization and connected region detection to obtain a particle contour, and generating a corresponding two-dimensional point cloud expression; in order to realize efficient registration, a KMP heuristic point cloud matching algorithm is provided, and the corresponding relation between the particle point cloud and the template point cloud is effectively established; on the basis of matching, continuous deformation tracks of the simulated particles are predicted through multi-wheel displacement, detection of deformation values is achieved, and the deformed particles are removed to achieve the classification effect. According to the method, the precision and robustness of flexible structure modeling are improved while the point cloud matching efficiency is improved, and the method is suitable for a particle structure analysis task under a high-noise condition.

The application provides an intelligent image processing system and method for a car based on a perception algorithm model, and the method comprises the following steps: S1. spatio-temporal reference double anchoring and dynamic intrinsic extrinsic parameter calibration of a vehicle-mounted image acquisition node; S2. multi-node image heterogeneous domain normalization and adaptive preprocessing based on a self-adaptive kernel regression non-local mean denoising algorithm; S3. hierarchical image feature extraction and semantic anchoring based on a graph neural network dynamic feature interaction network; S4. cross-node and cross-frame feature mutual checking and pseudo-feature elimination; S5. full-scene semantic completion and dynamic target trajectory prediction based on a variational autoencoder trajectory prediction model; S6. dynamic lightweight adaptation and algorithm power adaptive scheduling of the perception algorithm model; and S7. risk scene grading identification and image targeted enhancement output based on semantics and trajectories. The application provides stable, accurate and efficient vehicle-mounted image perception support for intelligent driving of a car, and improves the safety and adaptability of environmental perception of intelligent driving.

The invention discloses a robot visual navigation system and method based on Transform and dynamic feature optimization. The robot visual navigation system comprises an image acquisition module, an image feature matching module, a path point prediction module, a path point optimization module and a motion control calculation module. According to the invention, by introducing Transform and a dynamic feature optimization mechanism, high-quality processing and accurate modeling are carried out on image data; non-local mean denoising is adopted, ORB feature extraction and matching are improved, and mismatching points are filtered in combination with an RANSAC algorithm; a handshake synchronization mechanism between an image and path prediction is designed, and it is ensured that an image used for path point prediction is the latest collected image all the time by comparing timestamps; through outlier detection, clustering and geometric center calculation, the system can optimize path point selection in real time according to environmental changes, invalid paths are automatically avoided, and the continuity, rationality and safety of navigation paths are improved.

The invention belongs to the technical field of cytobiology and optical imaging, and particularly relates to a protein point recognition and analysis method and device based on real-time cell imaging. The protein point recognition method based on real-time cell imaging comprises the following steps: S11, acquiring a cell fluorescence image, performing denoising processing on the cell fluorescence image by adopting a non-local mean denoising algorithm, and removing background nonuniformity by using morphological opening operation to obtain a preprocessed image; and S12, dynamically calculating a threshold value of the preprocessed image based on local image intensity, carrying out binaryzation to obtain an image with a background and a foreground, carrying out morphological operation on the image to remove noise and artifacts, and carrying out image region segmentation on the image to obtain an image with a plurality of protein points. According to the method, a non-local mean denoising algorithm and morphological background correction are combined, so that the quality of the low-signal-to-noise-ratio cell fluorescence image is remarkably improved.

The application discloses a non-local mean-based adaptive SAR interferogram filtering method, and relates to the technical field of synthetic aperture radar. Firstly, two input SAR images are used to generate an interferogram and calculate the correlation coefficient of each pixel to form a correlation coefficient matrix. Phase information of the interferogram is extracted. Pixels that do not need filtering are screened. For pixels that need filtering, different search window radii are determined according to the correlation coefficient values, and the search window radius corresponding to the pixels that do not need filtering is set to 0. For each pixel that needs filtering, the weight of each pixel in the corresponding search window is determined, and the filtering result of each pixel is obtained by using a weighted average method to form a filtered SAR interferogram.

The present invention provides a medical image segmentation method and system for chronic obstructive pulmonary disease, belonging to the field of medical image processing, including obtaining a chronic obstructive pulmonary disease image and converting it into a grayscale image, performing a non-local means filtering operation on the grayscale image to obtain a filtered image; constructing a corresponding two-dimensional histogram based on the filtered image and the grayscale image; introducing an adaptive transfer strategy and a spiral motion operator into the artemisinin algorithm to obtain an improved artemisinin algorithm; using Renyi entropy as the objective and the improved artemisinin algorithm as the search method to perform threshold search to obtain the optimal segmentation threshold combination; segmenting the chronic obstructive pulmonary disease image according to the optimal segmentation threshold combination and outputting the segmentation result. It provides accurate technical support for the early diagnosis of chronic obstructive pulmonary disease.

The present invention discloses a method and system for analyzing the sclerosis ratio of kidneys based on MRI images, belonging to the technical field of medical image processing, including: selecting an MRI imaging sequence according to the kidney lesion characteristics of a patient; collecting images of the patient's kidneys based on the MRI imaging sequence to obtain different MRI imaging sequence images; fusing the MRI imaging sequence images to obtain a multi-modal image; preprocessing the multi-modal image, including per-pixel signal intensity correction, histogram equalization, adaptive filtering, and non-local means denoising; performing image segmentation on the preprocessed multi-modal image to obtain a binary image including a sclerosis region and a normal region; obtaining the kidney sclerosis ratio based on the binary image. The method of the present invention can improve the recognition accuracy and quantification precision of the kidney sclerosis region, more accurately evaluate the degree of kidney sclerosis, provide strong support for clinical research, and improve the medical level.

The invention discloses a greenhouse crop disease image segmentation method and system, relates to the technical field of image processing of ground scenes, solves the problems of low segmentation precision and the like in agricultural scenes with complex disease spot forms and the like in the prior art, and improves the segmentation precision, the processing speed and the robustness. Disease images of greenhouse crop disease plants are obtained and preprocessed; performing graying and non-local mean filtering processing on the disease image to obtain a two-dimensional histogram; taking the maximized two-dimensional Kapur entropy function as a target function, and segmenting the two-dimensional histogram by using a Kapur entropy threshold method to obtain an initial threshold set; searching the initial threshold value set by utilizing an improved rime algorithm to obtain an optimal threshold value set; and carrying out multi-threshold segmentation to obtain a disease image in which a lesion area is segmented, and completing greenhouse crop disease image segmentation. The method is suitable for processing strawberry disease images in the field of greenhouse crop disease image processing.

The invention belongs to the technical field of skin analysis, and particularly relates to a skin classification method fusing smoothness and spot recognition, which comprises the following steps: acquiring a user skin image through a mobile terminal, and outputting a standardized image after self-adaptive histogram equalization and non-local mean filtering preprocessing; a smoothness feature vector and a spot feature vector are extracted in parallel based on a standardized image, a fusion feature vector is obtained through calculation based on the smoothness feature vector and the spot density feature vector, and after the fusion feature vector is input into a skin classification model, key parameters can be extracted and decoded based on the obtained fusion adjustment vector. Depending on a spot density enhancement value, smoothness principal component strength and comparison between a covariant weight parameter and a threshold value, the skin type of the user is determined hierarchically, and decoding based on the smoothness principal component strength can filter noise and is more sensitive than a smoothness mean value.

The application discloses a kind of automatic control method and equipment for sweet potato seedling seedling pipeline operation, it utilizes differential pulse modulation to collect the excited state and background state image of vine, through space self-adaptive weighted difference modeling and non-local mean regularization enhancement technology, effectively suppress environmental light noise and retain weak biological fluorescence signal, to generate the high confidence vine node distribution map.On this basis, abandon traditional fixed-length cutting mode, construct dynamic programming model based on agronomic constraints, solve the optimal segmentation strategy of maximum output rate in the whole vine range.Finally, through the space-time fusion of visual coordinates and conveyor encoder, drive multi-axis servo system to track and accurately work the real-time virtual cutting point of dynamic movement, realize the efficient, low-loss automatic production of sweet potato seedling.

The invention relates to the technical field of intelligent image detection, in particular to a Graph mamba-based two-dimensional wavelet multi-scale high-resolution image anomaly detection system, which comprises a two-dimensional wavelet transform multi-scale high-frequency automatic self-encoding model, a high-frequency detail feature extraction model, a high-frequency detail feature extraction model and a multi-scale high-frequency automatic self-encoding model, and is used for carrying out multi-scale decomposition on an input high-resolution image to extract high-frequency detail features; generating corresponding multi-scale sub-image embedded data; and the input end of the dynamic characteristic calibration module is connected with the coding output end of the two-dimensional wavelet transform multi-scale high-frequency automatic self-coding model. According to the method, the two-dimensional wavelet transform multi-scale high-frequency automatic self-encoding model is combined with the Butterworth high-pass filtering and Marr kernel function, so that the extraction of abnormal related edge detail features is enhanced, and the detection accuracy is improved; the dynamic feature calibration module rejects inconsistent feature points through cosine similarity verification, filters redundant information in cooperation with a non-local mean de-duplication algorithm, reduces the data processing amount, and optimizes the calculation efficiency.

The application provides a RAW domain non-local mean image denoising method, comprising the following steps: S1, collecting a RAW image by a sensor; S2, dividing the image into N channels, such as R, Gr, Gb and B channels according to an actual Bayer format; S3, performing non-local mean filtering on each channel; further comprising: S301, calculating the similarity between each channel pixel point and the center pixel point of a neighborhood window; S302, calculating the weight according to the similarity information; S303, calculating the filtering result of each pixel point in each channel according to the weight; S4, merging the results of each channel after filtering into a RAW image, and outputting the result. The application utilizes the original characteristics of noise to perform image filtering in the RAW domain, reduces the difficulty of noise suppression, and has smaller side effects under the premise of achieving the same filtering effect; the non-local mean filtering is adopted, so that the problems of pattern noise after filtering, and false color and color cast caused by unbalanced channel filtering results are avoided.

The present invention relates to a high-resolution distributed seawater temperature monitoring method, which includes the following steps: S1, forming a two-dimensional temperature matrix by using multiple groups of distributed Raman signals obtained in consecutive multiple time frames; S2, performing noise reduction on the two-dimensional temperature matrix by using a non-local mean algorithm, retaining the overall temperature trend of the seawater area to be measured temperature in the two-dimensional temperature matrix, and obtaining a two-dimensional temperature matrix after the first noise reduction; S3, performing noise reduction on the two-dimensional temperature matrix after the first noise reduction by using a total variation denoising algorithm, retaining the details of the temperature change area in the two-dimensional temperature matrix after the first noise reduction, and obtaining a two-dimensional temperature matrix after the second noise reduction; S4, adding the collected distributed Raman signal as a new row after the last row of the two-dimensional temperature matrix after the second noise reduction to form a two-dimensional temperature matrix of the next time frame, performing noise reduction by using the non-local mean algorithm, and taking the last row as the current temperature of the seawater area to be measured temperature.

The present invention belongs to the field of bistatic interferometric SAR technology, and specifically relates to an adaptive spatial domain filtering method based on a navigation satellite bistatic interferometric SAR system. The invention first defines a search area for PS points and then performs non-local mean filtering on each PS point based on the search area, thereby achieving adaptive spatial domain filtering. This method solves the problem of eliminating multi-source errors through spatial domain filtering due to the system's bistatic configuration changes, low signal-to-noise ratio, and two-dimensional resolution in GNSS-InBSAR systems, and plays an important role in the practical application of GNSS-InBSAR systems.

The present application relates to the technical field of machine vision, in particular to a kind of robust visual detection system of ton bag goods based on multi-cluster feature fusion and posture correction drive, the system includes non-local mean enhancement module, multi-feature clustering fusion module, geometric feature extraction module, dominant posture clustering module, posture correction transformation module and goods segmentation determination module, non-local mean filtering is carried out to the original image of ton bag goods to obtain enhanced image, the present application is equipped with non-local mean enhancement module and multi-feature clustering fusion module, can realize the efficient enhancement of ton bag goods image and multi-dimensional feature fusion, the space alignment weighted superposition of color cluster and texture cluster is generated Fusion segmentation chart, let the feature representation of ton bag goods homogeneous region be more comprehensive, accurate, effectively improve the integrity and effectiveness of geometric feature extraction, so that the feature analysis ability of system to ton bag goods image is significantly improved.

The present application relates to the technical field of image processing, in particular to a kind of cardiovascular medicine OCT image filtering enhancement method, the method comprises: by obtaining optical coherence tomography OCT image, and the classification distribution parameter and tissue distribution parameter of image, wherein classification distribution parameter is based on the light and dark change of original image and is obtained by classifying original image, and tissue distribution parameter is based on the tissue distribution of original image and is obtained by segmenting original image.Then based on the difference of classification distribution parameter and tissue distribution parameter, determine the noise influence factor of original image, and according to noise influence factor, original image is carried out non-local mean filtering, clear target image that inhibits speckle noise is obtained, finally, target image is enhanced, it is ensured that the OCT image after enhancement retains important structure details, and unnecessary noise interference is reduced.