137 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 a deep learning-based rapid ore sorting method under adaptive illumination intensity, and relates to the technical field of ore sorting, the deep learning-based rapid ore sorting method comprises the following steps: obtaining illumination intensity distribution information and ore three-dimensional point cloud data, and calculating ore space orientation parameters based on quaternion attitude estimation; inputting the obtained data into a layered illumination compensation network, and obtaining an illumination normalized enhanced image through adaptive brightness mapping and local light spot correction in combination with reflectivity recovery and non-local mean filtering; inputting the illumination normalized enhanced image and the ore three-dimensional point cloud data into a multi-branch feature extraction network, constructing feature high-order association, and adopting adaptive feature reconstruction and conditional random field optimization features; and inputting the reconstructed features into a classification model based on active learning, optimizing a feature manifold space by using a self-organizing mapping network, carrying out sample labeling in combination with active sampling of adaptive entropy threshold screening and uncertainty perception, and realizing ore classification and sorting through random forest integration.

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 bone joint lesion area automatic labeling method and system based on image processing, and belongs to the field of bone joint lesion area labeling. According to the invention, a bone joint reflection signal is obtained by combining the polarization-state-adjustable laser light source with polarization-sensitive optical coherence tomography, and a three-dimensional birefringence distribution diagram is generated through matrix analysis. And after non-local mean filtering is carried out on the distribution map, a lesion edge contour is extracted based on a dynamic gradient amplitude, and a binary mask is generated. Mask pixels are converted into network nodes containing space coordinates, gradient directions and birefringence, and a topological network is constructed based on the Euclidean distance of adjacent pixels. The weight is calculated through the node intersection density and the birefringence, and a graph attention network is utilized to aggregate multi-layer features to extract lesion topological features. The features are mapped to a three-dimensional space to construct a lesion probability distribution field, an interactive labeling result is generated through contour surface extraction and transparency mixed rendering, and the accuracy of labeling the complex lesion area of the bone joint can be improved.

The invention relates to the field of phytoplankton image edge detection, in particular to a marine phytoplankton image edge detection method based on a non-local mean denoising mode. The method comprises the following steps: putting a marine phytoplankton image file needing to be detected into a folder of a program item, automatically reading a code into an image in the folder by a program, and loading a to-be-processed image; edge details related to the marine phytoplankton image are reserved based on a non-local mean denoising algorithm; aiming at the image data after the edge details are reserved; carrying out edge extraction on the image based on a Canny edge detection algorithm; and outputting a marine phytoplankton image with edge details reserved. Through combination of non-local mean denoising and Canny edge detection, noise can be more effectively removed, image details and structure information can be kept, and the influence of noise on edge detection is reduced, so that clearer and more accurate edges are extracted; and in an image with relatively large noise, the edge extraction precision and the image quality can be remarkably improved.

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 image data processing, in particular to a top drive drilling anomaly detection method and system based on machine vision. The method comprises the following steps: acquiring gray values of all pixel points in a current operation image of the top drive drilling well in real time; constructing a reference block of each pixel point, marking any pixel point as a target pixel point, marking the reference block of the target pixel point as a target block, and selecting a plurality of reference blocks as neighborhood blocks of the target block; determining a noise value of the neighborhood block; determining the importance degree of the target pixel point; determining a weight optimization factor and an optimization weight of the neighborhood block; and de-noising the current operation image by using a non-local mean filtering algorithm, and carrying out anomaly detection through the trained deep learning model. According to the method, the image feature difference analysis between the neighborhood block and the reference block is introduced, and the weight calculation of the non-local mean filtering algorithm is optimized, so that the de-noising precision of the operation image of the top drive well drilling can be remarkably improved, and the accuracy of anomaly detection is further improved.

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 a knee arthritis image measurement model system and method based on deep learning, particularly relates to the technical field of image processing, and is used for solving the problem of low image measurement accuracy. According to the method, the image preprocessing technology and the deep learning segmentation method are integrated, high-precision automatic segmentation and analysis are performed on the knee joint image, the contrast ratio and the resolution ratio of the image are enhanced, meanwhile, the image quality is further improved through biased field correction and non-local mean value noise reduction, and the image segmentation is performed by adopting the U-Net model, so that the image quality is improved. A complex structure in the joint is effectively recognized, segmentation precision is improved, a segmentation strategy is dynamically adjusted according to measurement influence information generated in the segmentation process, the processing flow is optimized in real time through different regulation and control signals, diagnosis efficiency and accuracy are improved, and therefore the automation level and reliability of medical image processing are improved, and the medical image processing efficiency is improved. Efficient and accurate support is provided for clinic, and the accuracy of osteoarthritis image measurement is improved.

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 seismic signal denoising method based on Surelet transformation, and aims to effectively suppress noise and reserve useful signal details. The method comprises the following steps: firstly, preprocessing input seismic data to remove irrelevant parts and standardize the irrelevant parts so as to enhance the stability of the data; and then, decomposing the signal into sub-band coefficients of multiple scales and directions by using curvelet transform, and introducing an SURE algorithm to automatically select an optimal threshold value so as to maximize a denoising effect. And then, carrying out soft threshold or hard threshold processing on each sub-band coefficient to suppress noise. In order to further improve the denoising effect, a fast non-local mean (FNLM) algorithm is adopted to reduce residual noise, and gradient information weighted smoothing is combined with a directional smoothing diffusion algorithm. And finally, reconstructing the de-noised seismic signal through inverse Surelet transformation. According to the method, the defects of a traditional denoising technology are effectively overcome, the wide application potential in earthquake monitoring and analysis is shown, and the signal definition and interpretation accuracy can be remarkably improved.

The invention relates to a high-resolution seawater distributed temperature monitoring method. The method comprises the following steps: S1, forming a two-dimensional temperature matrix by a plurality of groups of distributed Raman signals acquired in a plurality of continuous time frames; s2, noise reduction is carried out on the two-dimensional temperature matrix through a non-local mean algorithm, the overall temperature trend of a seawater area to be subjected to temperature measurement in the two-dimensional temperature matrix is reserved, and the two-dimensional temperature matrix after primary noise reduction is obtained; s3, noise reduction is carried out on the two-dimensional temperature matrix after primary noise reduction through a total variation denoising algorithm, details of a temperature change area in the two-dimensional temperature matrix after primary noise reduction are reserved, and a two-dimensional temperature matrix after secondary noise reduction is obtained; and S4, adding the collected distributed Raman signals as a new line to the last line of the two-dimensional temperature matrix after secondary noise reduction to form a next time frame two-dimensional temperature matrix, reducing noise through the non-local mean algorithm, and taking the last line as the current temperature of the seawater area to be subjected to temperature measurement.

The invention provides a non-local mean filtering and adaptive difference-based chip defect detection method, which comprises the following steps of: firstly, collecting image data of a chip to be detected through an industrial camera, and preprocessing to improve the image quality; and then, calculating a local entropy of each pixel to dynamically adjust a smoothing parameter of non-local mean filtering, and carrying out image denoising through the non-local mean filtering. Thirdly, a background image is obtained through a Gaussian filter, and a difference image is obtained through difference operation; and dynamically adjusting a difference threshold based on local frequency information, carrying out binarization processing on the difference image, and extracting a defect area. And finally, marking and classifying the detected defects through connected region analysis and contour detection. According to the method, by combining the self-adaptive non-local mean filtering of the local entropy and the self-adaptive difference threshold value method based on the local frequency, the denoising effect is improved, and the accuracy and robustness of defect detection are remarkably enhanced. According to the method, the limitation of a traditional chip defect detection method in a complex scene is overcome, and effective technical support is provided for chip quality control in actual industrial production.

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 an underwater image enhancement method, system and device combined with attenuation channel correction, and the method comprises the following steps: carrying out the color correction of an image through employing an attenuation channel adaptive correction white balance technology, and recording the image as a first enhancement image I1; de-noising the first enhanced image I1 by using a fast non-local mean algorithm based on a local block, and recording the de-noised first enhanced image I1 as a second enhanced image I2; improving the contrast ratio of the first enhanced image I1 by adopting a self-adaptive stretching method based on the local characteristics of a histogram, and recording the first enhanced image I1 as a third enhanced image I3; and obtaining a final enhanced image by using a bilateral weight fusion strategy in combination with the complementary advantages of the second enhanced image I2 and the third enhanced image I3. The system and the equipment are used for executing the method. According to the method, qualitative and quantitative experiment results on different underwater image reference data sets show that the contrast of the image can be uniformly improved and details and textures of the image can be enhanced while the color of the image is corrected.

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 invention relates to the technical field of construction quality detection, in particular to an electromechanical pipeline construction quality inspection system and method based on an unmanned aerial vehicle. Comprehensive inspection of pipelines and supports in a construction site is achieved through autonomous flight of an unmanned aerial vehicle. The method comprises the steps that route planning is conducted based on construction site structure data; the unmanned aerial vehicle flies according to a preset route, and the oblique photography camera collects multi-angle image data; denoising is carried out through a non-local mean value algorithm, the image contrast is enhanced through histogram equalization, and in combination with weighted fusion of local and global features, feature point matching and splicing of multi-view images are realized; analyzing the spliced image by using a DeepLabV3 + semantic segmentation algorithm, extracting geometric characteristics of the pipeline and the bracket, and detecting potential hazards based on an inclination angle calculation formula of the suspended pipeline and the bracket; a detection result is fed back to related personnel and is displayed in the monitoring system in real time; according to the invention, the efficiency and precision of construction site potential safety hazard detection are comprehensively improved.

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.