71 results about "Image noise reduction" patented technology

The invention relates to the technical field of metal detection, and discloses a polarization imaging metal surface scratch identification system. The system comprises a polarization imaging unit, an image preprocessing unit, a polarization feature extraction unit, a scratch recognition unit and a parameter self-adaption unit. The polarization imaging unit collects and transmits a metal surface polarization image; the image preprocessing unit performs noise reduction and enhancement on the image to generate an optimized image; a polarization feature extraction unit analyzes polarization state parameters of the optimized image to obtain polarization degree and azimuth angle distribution, and extracts polarization features of the scratch candidate area; the scratch identification unit compares the polarization characteristics with a preset template to generate an identification result; and the parameter adaptive unit dynamically adjusts the acquisition parameters of the polarization imaging unit according to the definition and contrast ratio of the scratch in the recognition result. According to the system, the polarization imaging technology is utilized, multi-unit cooperative work is combined, metal surface scratches can be accurately recognized in a complex environment, the system adapts to different detection conditions, and the recognition effect is improved.

The invention relates to the technical field of unmanned aerial vehicle image processing, and discloses an unmanned aerial vehicle image noise reduction and color enhancement optimization method and system. The method comprises the following steps: firstly, acquiring an original image sequence acquired in the flight process of the unmanned aerial vehicle, and identifying corresponding environment illumination intensity change data and motion fuzzy feature distribution; time dimension features of the original image sequence are divided based on environment illumination intensity change data, and a noise feature distribution map is constructed in combination with motion blur feature distribution; and finally, calling an equipment imaging parameter set of the original image sequence, analyzing a corresponding imaging mode constraint condition, and formulating a multi-stage noise reduction strategy framework for the original image sequence according to the constraint condition and the noise feature distribution map. The method realizes effective noise reduction and color enhancement of the unmanned aerial vehicle image by comprehensively considering the environment illumination change, the motion blur feature and the equipment imaging parameter, improves the image quality, and is suitable for the field of unmanned aerial vehicle image processing.

The invention discloses pavement crack detection equipment based on double spectrums, and the equipment comprises an equipment design module which comprises a camera module, a supporting structure, an inclination angle monitoring module, an image processing unit and a mobile device; the dual-spectrum fusion detection module comprises a thermal diffusion enhancement dual-spectrum multi-scale feature fusion image preprocessing module, noise reduction and multi-scale feature fusion are carried out on images collected by the infrared thermal imaging camera and the visible light camera, and a detection result and category and position information are output through an unsupervised enhancement BiSeNet hybrid wavelet transform network to complete detection; and the pavement quality evaluation module is used for realizing pavement crack detection through image splicing and BiSeNet network segmentation, realizing pavement quality evaluation through crack distribution diagram generation and area proportion calculation, and realizing detection result display through visual labeling. According to the invention, the detection efficiency can be greatly improved, the maintenance cost can be reduced, and powerful support can be provided for timely maintenance of the road.

The invention discloses a tunnel face integrity evaluation method based on deep learning, and the method comprises the steps: firstly scanning a tunnel face through a laser radar carried on an unmanned plane, obtaining a point cloud file of the tunnel face, and carrying out the data registration, data noise reduction, data simplification and three-dimensional modeling of the point cloud file through a data processing technology; the method comprises the following steps: performing tunnel face image extraction and image preprocessing on a three-dimensional model obtained after data processing, performing crack identification through a YOLOv5 convolutional neural network, and performing enhancement and structure optimization on crack image features through operations such as image segmentation, image noise reduction, crack skeleton extraction, edge detection, crack skeleton line splicing and the like. And information such as crack width and crack length is obtained through the processed image. The face fracture density and the ratio of open, micro-open and closed fractures are calculated through the information, the face fracture density is used for distinguishing the completeness and local stability of the tunnel face rock mass, and the completeness and local stability of the tunnel face are evaluated more comprehensively through the ratio of the three fractures.

The invention relates to the technical field of atmospheric pollution monitoring, in particular to an image pollution value monitoring method and device, and the method comprises the steps: obtaining image data collected by a camera, and carrying out the preprocessing of multi-frame image noise reduction and light and shadow correction on the image data; performing feature extraction on the preprocessed image data to obtain global semantic features and local sparse features; performing dynamic weighted fusion on the global semantic features and the local sparse features to obtain fusion features; stripping interference features in the fusion features to obtain final fusion features; and inputting the final fusion feature into a deep full-connection network for analysis, and outputting various types of pollution values. It can be understood that according to the technical scheme, the image is preprocessed in advance, so that the image becomes an effective data source, the scene adaptability is improved, a double-model parallel architecture and interference stripping are achieved, the feature extraction and fusion precision is remarkably improved, and the prediction precision is improved.

A filter unit decomposes image data from an image sensor adapted to capture images at a predetermined frame rate into a low-frequency component and a first high-frequency component. A motion vector estimation unit reads data from an event sensor, adapted to asynchronously output information on a pixel in which a brightness changes, at a frame rate higher than the predetermined frame rate and estimates a motion vector. A motion compensation unit performs motion compensation based on the motion vector. The filter unit generates a third high-frequency component by adding the first high-frequency component and a second high-frequency component extracted from an image obtained by motion compensation at a predetermined ratio, and reduces a noise in the image data by adding the low-frequency component and the third high-frequency component.

The invention discloses a TGV glass substrate double-axis synchronous stable scanning detection method, which comprises the following steps: firstly, importing hole site coordinate data of a TGV glass substrate, dividing a dense sub-region and a sparse sub-region according to hole site space distribution density, generating a variable density scanning path, and realizing dynamic adaptation of detection speed and resolution; then the double-shaft synchronous driving mechanism drives the substrate to move, and the multi-degree-of-freedom visual scanning mechanism automatically switches the light source state and the camera rotation angle according to the type of the detected target and synchronously collects images; the adsorption pressure is monitored in real time in the whole detection process, and when the pressure is lower than a safety threshold value, automatic speed reduction or alarm is performed to guarantee substrate safety; and finally, defect identification and classification are completed through multi-frame image noise reduction, feature extraction and a support vector machine algorithm. The problems that in traditional detection, efficiency and precision are difficult to consider at the same time, the substrates are prone to damage, and the automation degree is low are solved, efficient, high-precision and low-damage batch detection of the TGV glass substrates is achieved, and the method is suitable for online detection scenes of mass production lines.

The invention relates to the technical field of deep convolutional neural networks, in particular to a cryoelectron microscope noise particle image detail reservation noise reduction method based on edge enhancement, which comprises the following steps: inputting a cryoelectron microscope noise particle image into a trained image noise reduction model, and outputting a noise-reduced cryoelectron microscope particle image; in the image noise reduction model, an initial feature map of a noise particle image of the cryoelectron microscope is extracted; inputting the initial feature map into a multi-scale feature extraction module to capture multi-scale structure characterization; inputting the multi-scale feature map into an edge enhancement module to detect and enhance macromolecule boundary features to generate an edge enhanced feature map; inputting the edge enhancement feature map into a detail retention module for processing and retaining structure details to generate a detail enhancement feature map, and performing convolution processing to generate a prediction noise component; and subtracting the predicted noise component from the cryo-electron microscope noise particle image to obtain a noise-reduced cryo-electron microscope particle image. According to the invention, the noise reduction quality of the noise particle image of the cryoelectron microscope can be improved.

The embodiment of the invention provides an SEM image noise reduction method. The method comprises the following steps: acquiring an SEM image to be denoised as an original image; constructing a noise reduction evaluation function to enable the noise reduction evaluation function to comprise a first item and a second item, and performing noise reduction processing on the original image by using the noise reduction evaluation function to obtain a corresponding noise reduction image; wherein the first item is used for reducing the pixel value fluctuation of the noise reduction image, and the second item is used for reducing the difference between the original image and the noise reduction image. Compared with the prior art, the method has the advantages that more image details in the original image are reserved on the basis of realizing image noise reduction, and the image quality of the noise-reduced image is improved.

The embodiment of the invention provides an image noise reduction method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring an original image; wherein the original image comprises at least two channels; for each channel, determining image data of the channel under at least two preset resolutions according to the original image; according to the image data of the channel under the at least two preset resolutions, determining denoised data corresponding to the channel; and according to the denoised data corresponding to each channel, determining a denoised image corresponding to the original image. For different channels in the original image, image data under different resolutions are obtained, information of multiple scales in the image is reserved, for each channel, independent noise reduction is performed on the channel in combination with the image data under each preset resolution, mutual influence of the channels is avoided, and the image noise reduction precision is effectively improved.

The present application relates to the field of signal detection, and particularly to a non-cooperative unmanned aerial vehicle burst frequency point detection method and device based on a Seg-Yolo model. The Seg-Yolo model proposed by the present application does not require prior sequences of non-cooperative unmanned aerial vehicle signals at all, and does not require prediction of specific sequences, and can directly realize continuous, discontinuous, overlapping and non-overlapping signal frequency point detection in a time-frequency graph. Through preprocessing, image noise reduction and the Seg-Yolo model, the present application reduces the image volume that needs to be processed and simplifies the size of the deep learning network, can accurately lock the center frequency of each frequency point on the basis of reducing the training time and improving the training efficiency, and improves the accuracy and reliability of signal extraction based on complex frequency hopping signals.

An image noise learning server includes an image input interface configured to receive training images, and at least one processor configured to control an image extractor to extract, from the training images, a first image including a stationary object and a second image including a moving object, a noise filter to obtain a third image by applying noise filtering with a first intensity to the second image, the third image including the moving object, a labeling unit to determine an intensity of a side effect based on a difference between the stationary object included in the first image and the moving object included in the third image, and a machine learning unit to receive, as a label, the determined intensity of the side effect and image attributes of the training images, and obtain artificial intelligence (AI) parameters by performing machine learning on the second image based on the received label.

The invention discloses a cold region concrete dam dangerous case intelligent identification method based on image information, and the method comprises the following steps: constructing a cold region concrete dam dangerous case image noise reduction method through the comprehensive application of wavelet transform, improved non-local mean noise reduction and a bilateral filtering algorithm; through improvement of a multi-scale Retinex algorithm and fusion of gamma transformation and a contrast-limited adaptive histogram equalization algorithm, a cold region concrete dam dangerous case image quality enhancement method is provided. Based on the pre-processed high-quality image, combining with an improved YOLOv8 network structure, introducing an attention mechanism to enhance the feature extraction capability, and constructing an intelligent identification model for the dangerous case of the concrete dam in the cold region; according to the method, the recognition precision of typical diseases such as cracks and freeze-thaw damage is remarkably improved.

The application relates to the technical field of metal surface image processing, and discloses a metal plate pit defect detection method and system based on a dense connection network. The method comprises the following steps: obtaining a metal plate surface image to be detected; performing an image noise reduction task based on a defect response multi-scale attention enhancement mechanism; performing a multi-scale defect semantic feature extraction task; performing a defect region saliency enhancement task; constructing a multi-scale candidate defect frame set; and outputting a pit defect detection result set. Compared with the prior art, in the actual industrial environment under the conditions of a complex texture background or a high surface reflection, especially in the actual industrial environment with interference features such as oil stains, scratches, rust or light spots on the metal plate surface, it is difficult to realize stable recognition of tiny pit defects. Since the application constructs a dense connection network structure with multi-scale feature fusion capability and introduces a spatial channel double attention enhancement mechanism, the pit defect detection precision in a complex industrial scene is improved.

The invention discloses an overhead transmission conductor strand breakage defect detection method and system based on machine vision. The method comprises the following steps: acquiring an overhead transmission conductor image based on a high-resolution camera; carrying out image enhancement processing based on gray stretching; performing image noise reduction processing based on guide filtering; carrying out edge detection based on an improved Canny algorithm; performing image optimization based on morphological operation; the method can automatically perform strand breakage defect detection on a large number of overhead transmission conductor images, has the characteristic of high automation, can significantly reduce the interference of human factors on the detection result, and meets the requirements of industrial automatic detection.

The invention relates to the technical field of cable intermediate joint defect diagnosis, in particular to an X-ray multimode imaging intelligent diagnosis method, system and equipment for cable intermediate joint defects and a medium. Scanning the intermediate joint of the cable in a nondestructive testing mode to obtain scanning data; the collected image is analyzed and processed, and image noise reduction and enhancement are completed; building a defect identification model, training the defect identification model, completing multi-scale defect feature extraction, capturing continuous distribution features of defects in space, and carrying out defect detection on the intermediate joint of the cable; and constructing a mathematical model according to a defect detection result of the cable intermediate joint, and outputting the result. And the position of the defect can be accurately positioned, a worker can quickly find the specific area of the problem according to the positioning information, and the troubleshooting time and workload are reduced.

The invention relates to an emission tomography image processing method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a plurality of local medical images of a target medical image; each local medical image is obtained by splitting the target medical image in at least one dimension; predicting a noise degree quantized value corresponding to each local medical image; and based on the noise degree quantized value corresponding to each local medical image, performing image noise reduction processing on the target medical image to obtain the target medical image after noise reduction is completed. By adopting the method, the noise reduction effect of the image can be improved.

The invention relates to the technical field of computer vision, in particular to an image noise reduction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring image data to be denoised, and preprocessing the image data to be denoised to obtain preprocessed image data; the preprocessed image data is input to a preset image noise reduction model, a final noise reduction image is obtained, the preset image noise reduction model is obtained based on a gated texture convolutional neural network architecture and through training by using a preset joint loss function, and the final noise reduction image is obtained. The preset joint loss function comprises a weighted sum of a mean square error loss item and a learning perception image fragment similarity loss item. Therefore, the lightweight gated texture convolutional neural network is adopted as a noise reduction core architecture, and model optimization is performed in combination with the joint loss function fusing the pixel-level fidelity and the perception similarity, so that the problems of excessive smoothness of the image texture and reduction of the perception quality caused by a noise reduction method based on mean square error loss are solved.

To provide an image noise reduction method, an electron energy loss spectroscopy method, and an analysis apparatus that reduce the effort required to remove noise from measurement images before deconvolving the instrument function. [Solution] This is an image noise reduction method in which a computer performs the following steps: a learning condition determination step, which determines the learning conditions for machine learning the noise reduction model based on a generated image from which noise has been removed using a noise reduction model trained with the Noise2Void algorithm and an analyzed image from which the generated image has been subjected to inverse convolution of the device function; a machine learning step, which performs machine learning of the measured image, which is the training data, using the Noise2Void algorithm according to the determined learning conditions; and a noise reduction step, which removes noise from the measured image, which is the analysis data, using the noise reduction model trained in the machine learning step, before performing inverse convolution of the device function from the measured image, which is the analysis data.

The invention relates to the technical field of electronic seals, and particularly discloses a seal feature-based physical and electric homologous seal image processing method, which comprises the following steps of: acquiring a macro image, a de-noised RGB image and an electronic seal image of a target physical seal; performing feature extraction on the macro image and the RGB image after noise reduction to generate a unique identification code; binding and encrypting the unique identification code of the target physical seal and the RGB image after noise reduction, and uploading the hash value of the unique identification code of the target physical seal to a service platform; and scanning the current physical seal to generate a unique identification code hash value of the current physical seal, and calling the electronic seal image of the target physical seal for use after the unique identification code hash value of the current physical seal is successfully compared with the unique identification code hash value of the target physical seal. According to the invention, full-life-cycle homologous binding and anti-counterfeiting verification of the electronic seal and the physical seal can be realized, and the problem that the traditional electronic seal is easy to counterfeit and difficult to trace is solved.

This invention provides a noise reduction system and method for liquid rocket engine simulation test videos, addressing the technical problem that existing image noise reduction methods are insufficient to meet the practical engineering requirements of noise reduction in liquid rocket engine simulation test videos. The noise reduction system for liquid rocket engine simulation test videos provided by this invention employs a color space conversion module to convert the input original RGB format image frames into YUV format, achieving separation of luminance and chrominance components. The Y channel, containing the main noise, undergoes multi-step processing through a Y channel processing unit, forming a spatial-temporal joint adaptive mechanism. This enables spatial adaptation of edge weights and local variance to accommodate scenes with rapid motion, responding more quickly to changes in the current frame, and combating trailing phenomena. For the U and V channels, which contain only color information, bilateral filtering is used to perform lightweight noise reduction, balancing effectiveness and efficiency, significantly improving noise reduction performance and video stability.

Disclose a multiscale retinex and FASTICA combined infrared thermal image processing method, including the following steps: laser infrared thermal imaging; FASTICA image processing; multiscale Retinex image noise reduction; signal-to-noise ratio calculation.The FASTICA infrared thermal image processing method based on Retinex can enhance the infrared thermal image to display the key information of the image, and the effect of laser infrared nondestructive testing has obvious visual improvement, and other types of image optimization processing can also be carried out by this method.

The present disclosure relates to an image enhancement method based on noise reduction processing, and relates to the technical field of image processing; based on multiple to-be-processed images, corresponding image types are determined, based on the corresponding image types of the multiple to-be-processed images, first to-be-processed images and second to-be-processed images are divided, and different labels are configured for the first to-be-processed images and the second to-be-processed images, wherein the configured labels can represent the relationship between a noise reduction weight and an enhancement weight; then, based on the first to-be-processed images and the second to-be-processed images configured with labels, noise reduction and enhancement processing is respectively performed by using a pre-trained image processing model, and processed images are obtained. The technical solution effectively combines image noise reduction and image enhancement, realizes image enhancement based on noise reduction processing, and improves the noise reduction and enhancement effect of images.

The application discloses an image processing method, system, chip and storage medium. Firstly, the application reads local input image data, corresponding local image reference data and local noise characteristic data from an on-chip cache, then performs fusion and noise reduction processing according to the three kinds of local data to obtain local noise-reduced input image data, local fusion data and local updated noise characteristic data, merges the local noise-reduced input image data to obtain noise-reduced input image data, and updates the on-chip cache by merging the local fusion data and the local updated noise characteristic data. Thus, the application implements an image noise reduction technical solution adapting to hardware constraints under an ISP architecture mainly using stream processing, without relying on external DDR storage, with limited on-chip resources, which can significantly reduce system bandwidth occupation and power consumption, and can also realize light and effective noise suppression while ensuring real-time performance.

Noise reduction in images is provided by performing a noise reduction step on blocks of pixels within a video-processing pipeline. The noise reduction step consists of applying a discrete cosine transform (DCT) to the block of pixels, quantizing the resulting DCT coefficients, and performing an inverse of the DCT to the quantized coefficients. The output of that noise reduction step is a block of image pixels similar to the input pixels, but with significantly less image noise. Because the noise reduction step can be performed quickly on small blocks of pixels, the noise reduction can be performed in real-time in a video processing pipeline.

The application discloses an image feature extraction method and an image noise reduction method and related devices, and belongs to the technical field of image processing. The application can strengthen the connection between the local area and the global information of the features from the progressive angle of the large local receptive field and the global receptive field, so that the feature extraction method based on the self-attention mechanism can still capture the long-distance pixel dependence while reducing the calculation complexity, can well remove the image noise in the image noise reduction task based on the pixel level, and can maximize the retention of the detail information of the image.

The present disclosure provides a flat field correction method and device, electronic equipment and storage medium, and relates to the field of image processing, which comprises: acquiring multiple background pictures of different positions of an object to be detected; calculating first, second and third gray values of the multiple background pictures; generating a target background template image of the object to be detected according to the gray values of the multiple background pictures, the first, second and third gray values of each pixel point; and correcting an object to be detected image according to the target background template image. The method of the present disclosure does not need to guarantee the absolute blank state which is difficult to achieve when obtaining the correction template in the past, and the background template image is generated one by one according to the background pictures of the object to be detected, so that the noise reduction template is more targeted, and the quality of image noise reduction is improved.

The invention provides a liquid rocket engine simulation test run video noise reduction system and a noise reduction method, which are used for solving the technical problem that an existing image noise reduction method is difficult to meet the engineering actual demand of liquid rocket engine simulation test run video noise reduction. According to the liquid rocket engine simulation test run video noise reduction system provided by the invention, the color space conversion module is adopted to convert an input image frame in an original RGB format into a YUV format, separation of a brightness component and a chrominance component is realized, a Y channel containing main noise is subjected to multi-step processing through the Y channel processing unit, and the noise is reduced. A space domain-time domain combined adaptive mechanism is formed, so that the edge weight and the local variance realize space domain self-adaption to adapt to a strenuous motion scene, respond to the change of the current frame more quickly and resist a trailing phenomenon; lightweight denoising is performed on a U channel and a V channel which only contain color information by adopting bilateral filtering, the effect and efficiency are both considered, and the denoising effect and the video stability are remarkably improved.

The invention relates to the technical field of metal surface image processing, and discloses a metal plate pit defect detection method and system based on a dense connection network. The method comprises the following steps: acquiring a surface image of a to-be-detected metal plate; executing an image noise suppression task based on a defect response multi-scale attention enhancement mechanism; executing a multi-scale defect semantic feature extraction task; executing a defect area significance enhancement task; constructing a multi-scale candidate defect box set; and outputting a pit defect detection result set. In the prior art, under a complex texture background or a surface high reflection condition, especially in an actual industrial environment in which interference characteristics such as oil stains, scratches, corrosion or light spots exist on the surface of a metal plate, stable identification of tiny pit defects is difficult to realize. According to the method, the dense connection network structure with the multi-scale feature fusion capability is constructed, and a space channel double attention enhancement mechanism is introduced, so that the pit defect detection precision in a complex industrial scene is improved.

The embodiment of the invention discloses a noise reduction data set construction method and device and electronic equipment. The method comprises the following steps: acquiring daytime noiseless data, and constructing Poisson-Gaussian distribution data based on the daytime noiseless data; acquiring night noise data, and performing noise reduction processing on the night noise data according to the Poisson-Gaussian distribution data to obtain night noiseless data; obtaining target dark frame data, and constructing dark frame-Poisson distribution data based on the target dark frame data and the Poisson-Gaussian distribution data; and adding dark frame-Poisson distribution data to the daytime noiseless data and the night noiseless data, and constructing a target noise reduction data set. According to the embodiment of the invention, night data enhancement and dark frame correction modeling are fused, a high-quality noise reduction data set with better adaptability and generalization ability in a complex illumination environment is constructed more flexibly, and a solid foundation is laid for subsequent efficient, high-quality and high-reliability image noise reduction processing.