13 results about "Non-local means" patented technology

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 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 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 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.

This invention provides an adaptive window clustering method for shale MAPs based on a fuzzy neural network, relating to the fields of oil and gas development and image segmentation. Specifically, it includes the following steps: performing domain decomposition, nonlocal mean filtering, and gray-level normalization on the shale MAPs; dynamically adjusting the sliding window based on local gray-level variance; characterizing the shale micro-features within the sliding window into gray-level features and structural features using the gray-level co-occurrence matrix and structural parameters; and combining the gray-level features and structural features as a feature vector and inputting it into a fuzzy neural network (FNN) for micro-feature clustering. The technical solution of this invention overcomes the problem in existing technologies that cannot simultaneously consider the multi-scale micro-features and boundary uncertainties in shale MAPs.

The application discloses a low-illumination image enhancement method suitable for freight security monitoring scenes. The method first collects low-illumination original image data through a high-definition starlight-level camera, and carries out non-local mean filtering and linear normalization preprocessing; further, an improved discrete cosine transform filter bank is used for frequency domain transformation of the image, a filter bank with direction selectivity is designed based on image frequency characteristics, texture complexity and edge characteristics, so that multi-frequency domain and multi-direction features are extracted, and separation of noise and effective details is realized; subsequently, multi-source image features are adaptively fused through a fusion rule based on contrast weighting; finally, the fused features are input into a specially trained encoder-decoder structure convolutional neural network, and optimization training is carried out by using a multi-component loss function, so that deep enhancement and reconstruction of the image are completed.

This invention discloses a tool edge defect detection method based on image fusion. First, by designing a multi-path feature extraction backbone (FEH) and an adaptive feature fusion module, the model's ability to represent tool features is enhanced. Then, a hierarchical focusing module (HF) and a feature stitching interaction module (FCI) are introduced to optimize multi-scale feature fusion and detail perception performance, constructing a lightweight FEF-DETR detection model based on RT-DETR to achieve high-precision tool region localization. On this basis, non-local mean filtering (NL-Means) is used to denoise the extracted region, and adaptive dual-threshold Otsu segmentation and the Canny operator are combined to complete the fine detection of edge defects. This invention improves both detection accuracy and model efficiency in tool detection, enhances robustness in complex industrial environments, and makes tool edge features clearer, thereby improving segmentation accuracy.

This invention provides a method for multi-scale feature extraction and automatic identification of defects in weld X-ray inspection images, belonging to the field of non-destructive testing and image processing technology. The method includes: acquiring the original X-ray image of the weld and welding process parameters; constructing a process condition encoding vector and calculating an adaptive filtering kernel size for non-local mean filtering and noise reduction; calculating a histogram clipping and limiting coefficient based on material thickness parameters for block-based contrast-limited adaptive histogram equalization enhancement; extracting multi-scale feature maps through a backbone network and spatial pyramid pooling; fusing the process condition embedding vector with the multi-scale feature maps; employing a channel-space dual attention mechanism to enhance the feature response of the defect region; and outputting the defect location, type, and size through a detection head network, with feedback adjustment of enhancement parameters when the confidence level is below a threshold. This invention solves the problems of low sensitivity in detecting small-sized defects and poor generalization ability under different process conditions.

The application provides a fast non-local mean denoising method, device and equipment and readable storage medium, which comprises the following steps: for each to-be-denoised point in an original image, combining each to-be-denoised point with each reference point to form a transfer combination; for each transfer combination, calculating the real similarity of the neighborhood block of two pixel points; determining a current point and a search region; for each non-associated point in the search region, if the non-associated point and any to-be-denoised point form a transfer combination, determining a transfer path, calculating an approximate similarity according to the real similarity of each transfer combination involved in the transfer path, or calculating the real similarity; performing weighted average calculation on the pixel values of the pixel points in the search region to obtain the output value of the current point; and outputting a denoised image based on the output value of each to-be-denoised point. Through the application, the number of real similarity calculations is greatly reduced, thereby ensuring the denoising effect while significantly reducing the time consumption.

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 application provides a method for correcting a fixed reference surface of full-basin seismic results to a floating reference surface, and relates to the technical field of oil and gas exploration and development. The method comprises the following steps: cyclically traversing each point of seismic data, calculating the time difference of each point, and performing time correction based on a fixed reference surface time; constructing a floating reference surface, and performing interpolation modeling on the surface elevation by using shot point static correction and receiver point static correction; performing smoothing processing on the floating reference surface by using a Gaussian smoothing, recursive filtering, bilateral filtering, non-local mean filtering or Savitzky-Golay filtering algorithm; determining the replacement speed of each seismic data point according to the constructed floating reference surface and the real surface model, and performing calculation on each point by using a time-velocity-distance formula, so that the seismic data is corrected from the real surface to the floating reference surface. Finally, the correction results are checked and evaluated. The application improves the precision and efficiency of seismic data processing.

The application provides a wafer defect detection method and system based on YOLO-Label comparison, comprising: obtaining a wafer image to be detected, performing adaptive accelerated non-local mean filtering denoising and adaptive multi-scale gradient enhancement Canny edge detection on the wafer image to be detected, and obtaining a binary edge image; inputting the binary edge image into a pre-trained target detection model to obtain target positioning information in the YOLO-Label format, wherein the model is obtained by training a YOLOv8n network that is lightened by Ghost convolution and is adapted to a single channel, using a wafer binary image sample set; comparing the target positioning information of the image to be detected with standard positioning information of a defect-free image, extracting feature points of a bounding box, calculating an Euclidean distance matrix, and based on threshold matching, determining missing defects or redundant defects. The application can realize high-precision, high-efficiency and high-robustness wafer defect automatic detection with less labeled data under complex imaging conditions, and significantly improves the detection speed and integrity.