1124 results about "Image denoising" patented technology

This invention disclosed a full-space intelligent detection method and system for underground drainage networks, as well as storage media, including the following steps: image acquisition, intelligent image denoising, internal pipe defect segmentation, concealed defect detection around the pipe, 3D reconstruction with volume quantification, and pipeline life prediction. This invention introduced a bionic four-wheel-drive, all-terrain detection robot that can effectively navigate through mud and flowing water-challenges that hinder traditional detection devices. By leveraging deep learning algorithms as well as various techniques of computing vision, 3D reconstruction, and point cloud processing, the system thoroughly analyzed collected data to determine defect types and precise locations. Utilizing this analysis, precise location of different defect type and quantitative measurement of their dimensions can be realized. Based on the data analysis results, a deep-learning driven model was developed for predicting pipeline longevity to support maintenance staff with timely information on pipe defects and operational lifespan.

The invention discloses a power transmission line inspection image denoising method and system, and the method comprises the following steps: S1, obtaining an original image of an insulator, adding noise to the original image, and constructing an insulator noise image data set; s2, improvement is carried out based on an SADNet denoising network model, a Mix module, a CSM module and a CCAM module are introduced, and an SADNet-S denoising network model is constructed; s3, training the SADNet-S denoising network model by using the insulator noise image data set to obtain an optimal training weight; and S4, loading the optimal training weight into the SADNet-S denoising network model, and inputting the noise image of the insulator to be detected into the SADNet-S denoising network model to obtain a denoised insulator image. The invention provides an improved SADNet-S denoising network model based on multi-scale feature extraction, dynamic adaptive filtering and an attention mechanism, and by adopting the SADNet-S denoising network model, the strong generalization and robustness of the SADNet-S denoising network model are brought into full play, so that efficient image denoising is realized, and the target detection precision is remarkably improved.

The invention discloses a cancer image denoising method and system based on a multi-scale neural network, and the method comprises the steps: carrying out the adaptive segmentation of a cancer image, carrying out the noise estimation and statistical analysis of each segmented region, and automatically adjusting the denoising parameters; performing multi-scale feature extraction by adopting wavelet transform and pyramid decomposition, and analyzing and processing the image after parameter adjustment; performing quality evaluation and distortion detection on the image after multi-scale processing, automatically identifying and positioning artifacts and distortion, and correcting and compensating interference by adopting a point spread function inversion and Wiener filtering method to obtain a corrected image; and after histogram equalization and contrast-limited adaptive histogram equalization are applied to the corrected image, denoising processing is carried out by using a convolutional neural network in combination with sparse representation and a dictionary learning algorithm, and a denoising result is obtained. According to the method, noise in different types of cancer images can be effectively removed, meanwhile, key diagnosis information is reserved, and the image quality and definition are improved.

The invention relates to the technical field of image denoising, and particularly discloses a dual-domain heterogeneous image denoising method, which comprises the following steps: extracting a preliminary feature map based on depth separable convolution; an encoder of a double-domain heterogeneous cooperative architecture is adopted in a shallow layer of a hierarchical double-drive encoding and decoding architecture to perform double-domain heterogeneous cooperative processing, through hierarchical feature adaptation, the shallow layer gives consideration to details and local structures, a deep layer focuses on global semantics, and dynamic allocation of computing resources is performed, so that the redundant computing burden is remarkably reduced; and splicing the processed image frequency domain information and the image space domain information, fusing features of each layer after hierarchical processing based on a vertical stripe perception fusion attention mechanism module connected between an encoder and a decoder in a jumping manner, and outputting the fused features to the decoder to obtain the sensitivity of denoising image enhancement to vertical stripe noise. And a noise area is suppressed in a targeted manner.

The invention discloses a joint denoising training method combining a noiseless image and a noise image. The method comprises the following steps: S1, training an image denoising model: training the image denoising model by using the noiseless image and the corresponding noise image as training data; s2, data enhancement: performing multiple enhancement operations on the input image, wherein the enhancement operations comprise at least one of rotation, overturning, cutting and color change; s3, in a model training process, optimizing the network by using a mixed loss function; s4, implementing a dynamic optimization strategy in the training process: dynamically adjusting the learning rate of the model according to the current denoising effect so as to further improve the convergence speed and denoising performance of the model; and S5, updating the parameters by adopting a self-adaptive optimizer in the model optimization process. Based on the method provided by the invention, the general denoising performance of the image denoising model can be further improved.

The invention relates to the field of image data processing, and discloses a texture preserving type image denoising and enhancing method based on a generative adversarial network, which comprises the following steps: acquiring an original image signal, and calculating low-frequency sub-band data and high-frequency sub-band data by using discrete wavelet transform; calculating the gradient magnitude of the low-frequency sub-band data to generate a structural significance gradient map; establishing a reverse mapping relation based on the structure saliency gradient map, and generating a spatial self-adaptive dynamic gating threshold; performing statistical gating on the high-frequency sub-band data by using the dynamic gating threshold to generate a high-pass gain coefficient and a low-pass suppression coefficient; according to the method, cross-band modulation logic of the structure flow to the texture flow is established, so that the problem that weak texture signals are easy to lose under non-uniform illumination is solved, and non-structured noise filtering and structured micro texture restoration are realized on the premise of not depending on semantic tags.

The invention provides a recycled carbon fiber quality detection method based on machine vision, and the method comprises the steps: obtaining a surface image of a carbon fiber bundle through a machine vision method, and carrying out the preprocessing of the image, including image denoising and enhancement; for the preprocessed surface image of the carbon fiber bundle, extracting the morphological characteristics of the fiber bundle, including fiber bundle surface texture and fiber orientation distribution, for representing the morphological change of the fiber bundle under the reciprocating bending stress; acquiring a scattered light intensity distribution image on the surface of the carbon fiber bundle, and extracting spatial distribution characteristics of scattered light intensity through an image segmentation method, including uniformity and continuity of scattered light intensity distribution; if the fracture risk exists, positioning the identified high-risk area, and collecting image data of the high-risk area; according to the high-risk area image data, scattered light intensity distribution characteristics are extracted, a quantitative relation between scattered light intensity space distribution and the fiber bundle fracture risk is established, and a quantitative index for evaluating the fiber bundle fracture risk is obtained.

The present disclosure provides a zero-shot low-dose Computed Tomography (CT) image denoising method and apparatus based on strip diffusion model. This method includes building a strip diffusion model which uses high fidelity of the diffusion model to complete end-to-end denoising of low-dose CT images with different doses, thicknesses and devices. Particularly, in the training process, only the normal-dose CT images are required, greatly reducing the data reliance of the model, and model training across dose and thickness condition can be carried out with the data of only one scenario. In a sampling process, strip scanning strategy is used in combination with overlapped strip information and the input low-dose CT images to solve the maximum a posteriori problem, thereby sequentially generating denoising results. The present disclosure only uses simple convolution and attention architecture and carries out extensive experiments on the datasets involving different doses and thicknesses.

The invention discloses an image real-time denoising and enhancing method based on a deep neural network, and the method comprises the following steps: collecting image data of different modes in parallel through a plurality of sensors, and capturing dynamic scene features through the cooperative work of the sensors; performing adaptive division on the acquired multi-modal image data, evaluating the characteristics of each sub-region according to environmental factors, and dynamically determining the processing priority of each sub-region; for different sub-regions, dynamically selecting the most suitable deep neural network architecture for parallel processing; adjusting the processing frequency through a self-adaptive time window in combination with the time sequence characteristics of the image; pixel-level dynamic fusion is carried out on the denoised and enhanced sub-regions; processing parameters are adjusted through a real-time feedback mechanism, incremental optimization and fine adjustment of the neural network model are achieved, and finally high-quality image data are output. The invention aims to improve the image quality in a dynamic environment through multi-modal data acquisition, adaptive division and real-time optimization.

The invention discloses a low-dose CT image denoising method and system based on a conditional diffusion model, and the method comprises the steps: constructing a denoising network model, only employing a standard-dose CT image in a training process, and simulating a generation process of the standard-dose CT image through the training of the denoising network model. And abundant image priori knowledge is constructed. Meanwhile, the de-noising network model uses a U-Net encoder-decoder structure, a multi-scale cross axis attention mechanism is added in a decoder and an encoder, important image details are reserved in the de-noising process, irrelevant noise components are removed at the same time, and smooth areas and continuous boundaries of the image can be recovered. In addition, the frequency compensation block in the jump connection can compensate the insufficiency of medium-high frequency signals in the training process, so that the original structure and texture information of the image are recovered.

The invention discloses a generative adversarial network sonar image denoising method and device fused with comparative learning, and the method comprises the steps: constructing a generative network, inputting a noisy image, and carrying out the image denoising processing through a fusion attention mechanism module and a comparative learning module; constructing a discrimination network, inputting a noise-free image without noise and a de-noised image generated by the generation network, and carrying out image discrimination; s3, training an adversarial network composed of the generative network and the discriminant network, and storing final parameters after training is finished; and S4, inputting the noise image into the trained generative network to obtain a denoised image. The fusion attention mechanism module and the contrast learning module can improve the ability of the generative network to understand and process the input image, enhance the ability of the network to capture and retain the image details, and accurately restore the main body features and detail information of the noise image.

The invention relates to a hyperspectral image restoration method and device based on a cyclic decoupling model. The method comprises the following steps: constructing a hyperspectral image hybrid degradation mathematical model according to pre-introduced descriptive factors; deducing a plurality of solving targets by using the hyperspectral image mixed degradation mathematical model; constructing a hyperspectral image restoration network with a cyclic decoupling structure, designing a plurality of sub-loss functions based on the plurality of solving targets, and training the hyperspectral image restoration network according to the plurality of sub-loss functions to obtain a trained hyperspectral image restoration network; and restoring an image to be restored according to the trained hyperspectral image restoration network. By adopting the method, the denoising and defect information complementation of the hyperspectral image can be realized at the same time, and the image quality is greatly improved.

The invention provides a capacitive equipment fault diagnosis method based on an improved YOLO model, and relates to the technical field of electric power digital data processing, and the method comprises the steps: collecting an infrared image of capacitive equipment; denoising the image by using a median filtering algorithm; a YOLOv7 model is used to carry out identification and target extraction on the denoised image; clustering the processed infrared image by using an FCM algorithm; and the clustering result is evaluated by adopting an F-measure method. According to the method, efficient recognition of the capacitive equipment is achieved through image denoising and target extraction, clustering is conducted on the infrared images of the capacitive equipment through the FCM algorithm, the fault type is diagnosed, intelligent management and control of a power grid are achieved conveniently, the fault diagnosis efficiency is improved, and the risk of manual diagnosis is reduced.

The invention discloses a power grid transmission line tower inclination detection method based on remote sensing image semantic segmentation, and relates to the field of electric power safety and artificial intelligence. Comprising the following steps: step 1), preprocessing a remote sensing image, including shadow detection and image denoising; 2) performing semantic segmentation on the remote sensing image through an improved U-Net neural network, and extracting a tower body area and a tower top area of the power grid transmission tower; and step 3), according to semantic segmentation results of the tower body and the tower top of the transmission tower, calculating center point coordinates of the two areas, and according to the two center point coordinates, calculating the inclination of the tower. According to the invention, by using the remote sensing image and semantic segmentation technology, the rapid detection of the inclination condition of the pole tower in a large-range area can be realized.

The invention discloses an image denoising and self-adaptive enhancement method based on pixel discretization and illumination guidance, and relates to the technical field of image processing, and the method comprises the steps: carrying out the down-sampling and noise reduction of an input image, and obtaining a preliminarily denoised image through the deconvolution operation, fuzzy pixel discretization and residual serialization; estimating an illumination component from the preliminarily denoised image by using an illumination estimation network; calculating a reflection image based on the estimated illumination component and the preliminarily denoised image according to an image enhancement theory; based on a dynamic splicing mechanism, splicing the reflection image and the illumination component in the channel dimension to obtain a spliced image; and with keeping of image details and brightness information as a criterion, denoising and enhancing are carried out on the spliced image through a reflection denoising network, and a denoised and enhanced image is output. According to the invention, high-quality recovery of low-illumination images is realized, and the intelligent level of an electric power inspection system is improved.

The invention discloses an image denoising and fringe removing method based on blind spot regularization, which comprises the following steps of: constructing a double-output blind spot network, respectively taking image reconstruction branch output and fringe estimation branch output of the double-output blind spot network as regularization constraint terms, and carrying out joint modeling on a clear image and fringe components through implicit network prior, random noise is removed by using blind spot regularization; a direction representation shuffling technology meeting J-invariance is introduced, and image and stripe separation is realized through multi-direction feature decomposition and vertical direction feature enhancement; in the model reasoning stage, scale-adjustable feature resampling operation is carried out on the input features, the blind spot receptive field is expanded, and the image reconstruction quality is optimized; and outputting the denoised clear image and the estimated stripe component by alternately optimizing the joint loss function of the dual-output blind spot network. According to the method, the advantages of a traditional denoising method based on a model and a method based on self-supervised learning are combined, and a new thought is provided for denoising and stripe removing tasks.

The invention provides a marine radar image denoising method and device based on a multistage filtering algorithm, and the method comprises the steps: carrying out the preprocessing of a to-be-processed marine radar image, and obtaining a first image; performing edge gradient detection on the first image based on the gradient direction corresponding to each pixel point in the first image and the target identified in the first image, and performing noise detection on the first image based on the gray value corresponding to each pixel point in the first image after the edge gradient detection is passed; after the noise detection is passed, processing the first image by adopting bilateral filtering to obtain a second image; and image enhancement is carried out after image sharpening is carried out on the second image, and a denoised marine radar image is obtained. According to the method, multiple filtering technologies such as Gaussian filtering, median filtering and bilateral filtering are combined, multiple kinds of noise in the marine radar image are effectively removed, especially image edge and target information can be kept in a complex environment, and the target detection and navigation precision is remarkably improved.

The invention discloses a wafer probe mark accurate detection method based on deep learning, and belongs to the field of wafer probe mark detection, and the method comprises the steps: constructing a pin mark image denoising preprocessing network, employing a small target feature protection and enhancement strategy based on HSV color space and local contrast joint adjustment, and carrying out the recognition of a small target feature; denoising and contrast optimization are carried out on the needle mark image; a multi-scene training sample is generated through mosaic splicing and mix fusion; a dense small target enhancement module is introduced into the backbone network to enhance needle mark feature expression, and a multi-scale feature fusion module is arranged in the neck network to extract full-scale features; and establishing an anchor frame optimization system adaptive to the minimum needle mark target, and adopting an optimizer and learning rate collaborative optimization training strategy to realize model adaptive convergence. According to the method, high-precision detection and robust identification of the wafer probe mark can be realized under a complex background, and the detection accuracy and stability are remarkably improved.

The invention belongs to the technical field of image processing, and discloses a multi-feature enhancement fusion Mama image denoising method, which comprises the following steps: constructing a Mama denoising model for image denoising processing, the Mama denoising model comprising a shallow feature extraction module, a depth feature extraction module and an image reconstruction module; the shallow layer feature extraction module is used for extracting shallow layer features of the input image and performing normalization processing on the shallow layer features; the depth feature extraction module comprises a plurality of DFEGs and a sixth convolutional layer, and feature extraction is performed on the normalized shallow layer features step by step through the plurality of DFEGs; according to the invention, through a plurality of DFEGs in the depth feature extraction module, gradual feature extraction from a shallow layer to a deep layer is realized, and through cooperation of the Mama long-distance dependence capture unit, the local residual error module LRB and the channel attention mechanism CAB, the problem that global structure and local detail recovery are difficult to consider at the same time in a complex noise environment in the prior art is effectively solved.

The invention relates to a sonar image generation method and device based on a diffusion model, equipment and a medium, and the method comprises the steps: carrying out the multi-time noise addition of each sonar actual measurement image through a forward process based on the diffusion model, and obtaining a plurality of noise images with different noise degrees; a plurality of noise images generated by the same image and corresponding semantic guidance graphs are used as a group of training data, the training data are input into a sonar image denoising network constructed based on a reverse process of a diffusion model to train the sonar image denoising network, in the training process, under the guidance of the semantic guidance graphs, the noise images are denoised for multiple times, and the noise images are denoised for multiple times. Learning parameters in the sonar image denoising network are adjusted according to a denoising result predicted by the network and an error of a corresponding real noise image, and finally, randomly generated noise images and semantic images are input into the trained sonar image denoising network for multiple times of sampling. And generating a sonar image related to the semantic image. By adopting the method, the high-quality sonar image can be efficiently generated.

The invention relates to an autonomous image enhancement system, and aims to solve the problems that an existing image denoising and super-resolution technology is too high in dependence on large-scale annotation data, insufficient in model generalization ability and insufficient in detail recovery. The method comprises the following steps: firstly, aiming at an input original image, generating a plurality of different views through a data enhancement technology, and inputting the views into a ResNet encoder to extract multi-scale features; thirdly, constructing positive and negative sample pairs by utilizing a comparative learning module, maximizing the similarity of the positive sample pairs and minimizing the similarity of the negative sample pairs through an InfoNCE loss function, and completing self-supervised feature learning; then, the U-Net decoder performs up-sampling layer by layer and combines jump connection to recover image details and generate a de-noised or super-resolution image. And finally, combining the comparison loss and the reconstruction loss to optimize feature learning and image reconstruction performance. The method can be applied to processing of medical images, remote sensing images and video monitoring images, and the image quality is improved.

The invention discloses an image denoising method and system based on a differential equation and a deep learning network. The method comprises the following steps: inputting a noise image subjected to image preprocessing into a differential equation NODE model and an improved deep learning network UNet; extracting multi-level features of the noise image layer by layer through a multi-scale channel feature fusion module of an improved UNet network; attention is increased for shallow information in the network through an attention mechanism module; and carrying out continuous time dynamic modeling by using a NODE model, taking the improved UNet network as a neural network vector field in the NODE model, and simulating a continuous time evolution process through an ODE solver to obtain a clear image. Through the fusion of ODE modeling and the deep learning network, the dependence on large-scale annotation data is reduced, parameters are evolved and optimized by using continuous time characteristics, the generalization ability of the model to complex noise is improved, and a good denoising effect can be obtained under the condition of few sample training.

The invention discloses a VSP wellbore wave suppression method for improving U-Net based on physical constraints, which comprises the following steps: introducing apparent velocity constraints of an F-K transform domain, constructing physical constraint conditions, simultaneously realizing time sequence feature extraction and prediction of wellbore waves in combination with a GRU network, and learning a mapping relation between a noise image and a clean image by using the network so as to improve the suppression efficiency of the wellbore waves. The details and the structure of a noise image are accurately restored through a training network, the difference between a real de-noised image and network output is minimized so as to achieve the purpose of image de-noising, the model retains effective signals through a residual learning mechanism, and meanwhile precise suppression of shaft wave noise is achieved. The method provided by the invention can accurately identify and effectively suppress wellbore waves and reflected waves thereof in a complex VSP wave field, significantly improve the signal-to-noise ratio, reduce residual noise, improve data processing efficiency while improving data quality, provide technical support for high-precision seismic imaging under a complex geological condition, and have a wide application prospect. And meanwhile, a feasible solution is provided for popularization and application in an actual work area.

The invention discloses an infrared image denoising method and system based on artificial intelligence, and relates to the technical field of image denoising, and the method comprises the steps: collecting infrared image data, extracting frequency domain spatial features, carrying out Gaussian filtering, restoring the features to an image space, determining a high-frequency feature map, extracting image background information, and determining a low-frequency feature map. And splicing the high-frequency feature map through a generator, and carrying out two-dimensional transpose convolution operation by adopting an encoder based on a convolution transpose self-attention mechanism. According to the method, the frequency domain of an infrared image is converted into high-frequency and low-frequency characteristic decomposition, high-frequency characteristics are extracted based on a Gaussian high-pass filter, space structure information such as edges and textures can be effectively reserved, the importance of the high-frequency characteristics can be weighted through an attention mechanism, exploration of noise can be enhanced, and for the background part of the image, the resolution of the image is improved. The attention module can identify an area with excessive brightness fluctuation, and can synchronize transmission of secondary features in a noise removal process through residual features.

A computing system including one or more processing devices configured to receive an image generation prompt and a reference image. Over a plurality of denoising timesteps, the one or more processing devices compute a guided image by applying denoising updates to a generated image at a denoising diffusion model. At a subset of the denoising timesteps, computing the guided image further includes applying guidance updates to the generated image based on the image generation prompt, the reference image, and a generated image set. The one or more processing devices compute each guidance update by performing a forward pass and a backward pass in first and second integration timesteps. A size of the generated image set and numbers of the first and second integration timesteps are each equal to a predefined integration timestep count. The one or more processing devices output a final generated image computed in a final denoising timestep.

Methods and apparatus for end-to-end unsupervised multi-document blind image denoising is presented. The multi-document blind image denoiser removes various noise types from noisy documents without paired target cleaned documents and preserves the contents for optical character recognition. The end-to-end unsupervised multi-document blind image denoiser integrates a Mixture of Experts with a cycle-consistent GAN as the base network that effectively removes multiple types of noise, including salt & pepper noise, blurred and / or faded text, as well as watermarks from documents at various levels of intensity.

The invention provides a lightweight low-dose CT (Computed Tomography) (LDCT) image denoising method based on a Transform network, and belongs to the technical field of medical image processing. The method provided by the invention is called CT-Denomer, global context information in a CT image and local context details of complex space change can be captured, and a high-quality image is generated. A core of a framework of the CT-Denoimmer is a Transform module, and the CT-Denoimmer comprises two key components, namely a multi-head convolution transpose attention (MDTA) and a hybrid context feedforward network (MCFN). The MDTA module captures global context information in an image with linear computational complexity, while the MCFN module pays attention to multi-scale local static and dynamic context information through a series of enhanced context Transformer blocks (eCoT). Besides, an intelligent operation attention layer (OWALs) is introduced to cope with a complex and changing noise mode in the LDCT image, so that the network can optimize the denoising effect in diversified scenes. According to the method, the fine structure retention and noise suppression of the denoising result of the LDCT image reach advanced performance.

The invention provides a low-dose CTA image noise reduction model training method, a noise reduction method and equipment. The method comprises the following steps: acquiring cardiovascular and edge noise enhancement data of a low-dose CTA image based on a cardiovascular segmentation model and an edge extraction model; according to the data and the conventional dose CTA image, a cyclic generative adversarial network is trained based on target loss so as to train the cyclic generative adversarial network into a low dose CTA image noise reduction model, and the target loss comprises cardiovascular maintenance loss obtained based on a cardiovascular segmentation model and edge maintenance loss obtained based on an edge extraction model. According to the invention, the blood vessel and edge enhancement and the global noise reduction of the low-dose CTA image can be combined in the training process of the low-dose CTA image noise reduction model, so that the model can give consideration to the reservation of the fine structure of the image and the improvement of the overall image quality. Therefore, the application effectiveness and reliability of a low-dose CTA image noise reduction result obtained by adopting the model can be effectively improved, and the reliability of CTA clinical diagnosis is improved.

The invention relates to the field of cultural relic image recognition, in particular to a text image enhancement method based on multi-scale feature fusion and a residual attention mechanism, and the method comprises the steps: obtaining a historical material data set, marking a text region in the historical material data set, and constructing a real text image data set; generating an image construction synthesis data set with the same format as the historical material text region; introducing a plurality of noise types into the synthesized data set to simulate possible problems of an actual old text image; an improved U-Net network is provided for text image enhancement so as to better learn a mapping relation between a real image and a degraded text image; a multi-scale feature perception and extraction module is adopted to extract feature information in the image so as to improve the image contrast and solve the noise problem; the extracted features are further processed through a residual attention module, and important areas in the image are effectively concerned; the image features are further optimized through a feature enhancement module, and image details and contrast are enhanced; the image denoising effect of the text extraction model for the historical materials is better than that of an existing model.

The invention discloses a food image denoising method based on dynamic coding and efficient channel perception hybrid upsampling, and the method is characterized in that a dynamic coding module CAMixer and a channel perception hybrid upsampling module E-CAMixUp are cooperatively integrated, and an efficient channel perception hybrid denoising network ECAMixDNet is formed. According to the method, the CAMixer utilizes a learnable attention mechanism to adaptively adjust attention so as to contain more useful textures, the representation capability of convolution is improved, and the E-CAMixUp realizes high-quality image reconstruction under multi-scale noise perception through the collaborative design of dual-path feature reconstruction and adaptive channel interactive filtering, so that the image reconstruction efficiency is improved. The problem that artifacts are easily introduced in the reconstruction stage by an inter-channel noise distribution difference suppression mechanism is solved. The network extracts multi-scale features through a Swin Transform and an RBF attention mechanism, a decoder is combined with a residual module to realize high-quality reconstruction, food texture and color sensitivity are effectively kept, and visual restoration quality is improved.