918 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 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 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 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 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 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 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 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 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.

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.

The invention discloses an artificial intelligence denoising method for a speckle shearing interference image based on self-supervised learning. The method is a self-supervised image denoising method based on blind spot learning. According to the method, an improved self-supervised network architecture is adopted, pixel points of an image are input through a random mask and are replaced by neighborhood pixel values, and a blind spot training sample is constructed. The network only learns and predicts an original noise value of a masked pixel point in a training process, and does not depend on a clean image as a supervision signal, so that real self-supervised learning is realized. The de-noising method comprises the steps of constructing a data set meeting training requirements, performing de-noising model training by using a blind spot learning method and a self-supervised network, performing de-noising calculation on a speckle interferogram through a fully trained model, and finally obtaining a de-noised interference image. The method has the characteristics of simplicity and convenience in calculation and high processing speed, and can meet the requirement of large-data-volume image processing.

The invention provides a medical image denoising and segmentation integrated model trained based on a conductible diffusion method, and belongs to the field of medical image processing, and the model comprises an improved denoising and diffusion module which is used for receiving an initial medical image, predicting an original clean signal of the initial medical image based on an improved denoising and diffusion probability model, and obtaining a final denoised medical image; the joint motion and segmentation module is used for receiving the initial medical image, extracting features through a shared encoder, and performing joint learning through a motion estimation branch and a segmentation branch to obtain segmentation data; the cascade training mechanism carries out end-to-end training on the improved de-noising diffusion module and the joint motion and segmentation module through a derivable connection, gradient back propagation is realized by using a joint loss function, and cascade training is completed. According to the method, the problem that the quality of segmented medical images is reduced due to the fact that denoising and segmentation tasks cannot be collaboratively optimized due to the fact that a traditional denoising method cannot be guided in sampling and is difficult to carry out cascade training with a segmentation model is solved.

The invention relates to an underwater target detection method based on a pulse neural network, which can realize underwater target detection with high precision and low power consumption. By fusing the cross-stage partial network and the YOLO architecture, the problem of pulse degradation is effectively solved, and the feature extraction capability of the model is enhanced; in order to solve the problem of underwater noise interference, the pulse-based underwater image denoising method is designed, only integer addition is used in the method, a pulse neural network structure can be seamlessly embedded, and the quality of a feature map is enhanced; in order to solve the problem that a traditional normalization method is low in precision in the spiking neural network, separate batch normalization is provided, by independently normalizing a feature map in multiple time steps and optimizing a residual structure, the time dynamic state of the SNN can be effectively captured, and the detection precision is improved. The network shows excellent performance in underwater target detection, and compared with an artificial neural network of the same scale, the network has higher performance and lower energy consumption.

The invention is applied to the technical field of image denoising, and particularly discloses a low-dose CT image denoising generalization method based on a diffusion model. The method comprises the following steps: S1, constructing a low-dose CT denoising and generalization network model; s2, acquiring CT images of a low dose and a corresponding normal dose; according to the low-dose CT image denoising generalization method based on the diffusion model, the multi-stage diffusion model and the dynamic double-attention network are designed, the problem of error accumulation in a traditional single-stage method is effectively relieved through a cascade optimization mechanism, meanwhile, the dynamic double-attention network DDA-Net is combined, and the low-dose CT image denoising generalization method based on the diffusion model is obtained. A channel-space self-adaptive attention mechanism is utilized to realize cross-dose level unsupervised generalization ability, clinical variable dose scenes can be adapted without relying on pairing training data, key high-frequency details for diagnosis are reserved, and discrimination of anatomical edges and textures is enhanced through a selective scanning mechanism.

The invention discloses a mobile equipment image denoising method based on a double-branch residual sparse network, and belongs to the technical field of image processing, and the denoising method comprises a residual sparse module which captures local features in an image through mixed expansion convolution and residual connection, and reduces the number of parameters of a model and the calculation complexity at the same time; the attention guiding residual sparse module introduces a channel attention and pixel attention mechanism on the basis of the residual sparse block to adjust the weight of the feature map, pays attention to an important area in the image, and improves the denoising effect and the image quality; and the feature fusion module is used for realizing high-frequency detail reservation and noise suppression by combining an attention mechanism and an activation function through processing of the residual module after addition of double-branch outputs. According to the method, image denoising is carried out in resource-limited scenes such as unmanned aerial vehicles, and the problems of large parameter quantity, low calculation efficiency, insufficient detail reservation and the like of an existing denoising model are solved.

The invention provides a mobile phone shell defect detection method and system based on industrial vision, relates to the field of defect detection, and aims to suppress background noise interference through image noise reduction and image enhancement preprocessing on the basis of high-resolution industrial imaging and combine complementary feature extraction of a gradient direction histogram and a cavity convolutional network. And the local structure characterization capability of the tiny defects is enhanced. Further introducing Capsule Networks to dynamically model a spatial geometrical relationship of mobile phone shell state semantics, and utilizing an attention-driven cross-modal refined global interaction feature interaction mechanism to realize fine-grained alignment and coupling association between texture distribution features and semantic state features of the mobile phone shell, and finally, through an intelligent classification decision module, performing classification on the mobile phone shell state semantics. Various low-contrast and sub-pixel-level defect types under the complex texture background are accurately recognized, and the detection sensitivity and the algorithm robustness are synchronously improved.

The invention discloses an image denoising device based on adaptive local enhancement and dynamic multi-scale dependent fusion, and the device comprises a data preprocessing module which is used for carrying out the pixel normalization processing, color space conversion and size and resolution adjustment of a to-be-denoised image, and obtaining a standard image; the adaptive local convolution enhancement module dynamically adjusts a receptive field of a convolution kernel according to noise characteristics on the standard image, and extracts an original feature map on the standard image; the long and short term dependency modeling module based on beam scanning captures long and short term dependency on the original feature map by using a beam scanning technology, and fuses the long and short term dependency with a standard image to obtain a preliminary de-noised feature map; the multi-level representation information extraction module is used for extracting deep-level de-noising feature maps of different scales in the preliminary de-noising feature maps; and the refined reconstruction module carries out fusion and refined reconstruction on the original feature map and the deep denoising feature map, and outputs a noiseless image. According to the invention, efficient and accurate image denoising processing can be realized.

The embodiment of the invention provides a training method of an image denoising model, an image processing method and an image processing system. The training method of the image denoising model comprises the following steps: acquiring a sample standard image and a sample noise-added image; generating a sample reference image according to the sample standard image, inputting the sample noise-added image and the sample reference image into an initial image denoising model to obtain an initial prediction image, and determining noise mode information by the initial image denoising model according to coding feature information of the sample noise-added image and the sample reference image; denoising the sample noise-added image according to the noise mode information; adjusting model parameters according to the initial prediction image and the sample standard image to obtain a reference image denoising model; the sample noise-added image is input into a reference image denoising model, a target prediction image is obtained, and the reference image denoising model predicts noise mode information according to the sample noise-added image; and adjusting model parameters according to the target prediction image and the sample standard image to obtain an image denoising model.

The invention discloses a post-stroke depression risk prediction system based on cerebral small vascular diseases and inflammatory markers, and relates to the technical field of computer-aided engineering, the post-stroke depression risk prediction system comprises: a data acquisition module acquires CSVD image data, serum inflammatory markers and clinical baseline information; the data preprocessing module processes image denoising registration, fills up marker missing values and encodes clinical information; the CSVD feature extraction module extracts image omics features and quantifies severity; the inflammation marker module calculates statistical characteristics and inflammation intensity; the multi-dimensional fusion module integrates features and eliminates redundancy; the risk prediction module is trained by using an improved attention CNN-LSTM model; the result output module visualizes risk and intervention suggestions; the model dynamic optimization module updates parameters by incremental learning. According to the method, multi-source key data are integrated, the prediction accuracy and generalization ability are improved, clinical interpretation and dynamic adaptability are achieved, early recognition of post-stroke depression is assisted, and patient prognosis is improved.

The invention provides a dermatoscope image segmentation method based on an attention mechanism and UNet, and belongs to the technical field of medical image segmentation of image segmentation. The technical problem that a traditional method is insufficient in accuracy in lesion region segmentation is solved. According to the technical scheme, the method comprises the following steps: 1, data preprocessing: carrying out image denoising, image enhancement and division on a data set; step 2, constructing a dermatoscope image segmentation network based on an attention mechanism and UNet; 3, putting the processed skin disease image training set into a dermatoscope image segmentation network model based on an attention mechanism and UNet for training to obtain an optimal model; and 4, after training is completed, inputting the test set into the optimal model, and detecting a focus segmentation result in the skin disease image. The method has the advantage that high segmentation accuracy and robustness are guaranteed.

The invention relates to the technical field of biological microscopic imaging, and discloses a deconvolution super-resolution imaging method and device based on deep learning denoising and a medium. The method comprises the following steps: acquiring a to-be-denoised image of a to-be-imaged structure marked by fluorescent molecules; de-noising the image to be de-noised by using a preset image de-noising neural network to obtain a de-noised image; a training data set for training the network is generated by utilizing the optical switch characteristics of optical switch fluorescent molecules, and the optical switch fluorescent molecules are used for marking to-be-imaged structures of samples in the training data set; the emission spectrum of the fluorescent molecules is overlapped with the emission spectrum of the fluorescent molecules of the optical switch; and carrying out deconvolution processing on the denoised image to obtain a super-resolution imaging image. According to the invention, the large-scale and high-quality true value image is obtained by using the optical switch characteristic of the optical switch fluorescent molecules, and the denoising effect of the image denoising neural network is improved; and denoising the image by using the network, and then carrying out deconvolution on the denoised image to realize large-view living cell single-frame super-resolution imaging.

The invention relates to the technical field of ultrasonic image processing systems, and provides an ultrasonic image processing system based on three-dimensional reconstruction, which comprises an ultrasonic data acquisition terminal, an image processing terminal, a three-dimensional reconstruction terminal, a quality evaluation terminal and a doctor interaction terminal, the ultrasonic data acquisition terminal is used for acquiring ultrasonic original radio frequency data and a B mode image and synchronously acquiring probe position information of the ultrasonic probe; the image processing terminal is used for performing image denoising, edge enhancement and tissue feature labeling according to the ultrasonic original radio frequency data and the B mode image, and extracting key section information for subsequent reconstruction; the three-dimensional reconstruction terminal is used for performing spatial reconstruction based on the key section data and the probe position information to form a three-dimensional ultrasonic image body; the quality evaluation terminal is used for scoring the three-dimensional reconstruction effect of the three-dimensional ultrasonic image body and generating three-dimensional reconstruction quality evaluation information; and the doctor interaction terminal is used for a doctor to check the three-dimensional ultrasonic image body and the three-dimensional reconstruction quality evaluation information in real time and generate and display optimization suggestion information. The method has the effect of improving the accuracy of three-dimensional reconstruction quality evaluation.

The invention provides an unsupervised CT image denoising method and device based on multi-mode large model text prompt. The method comprises the following steps: performing preliminary denoising on an original CT image by using a pre-training coding and decoding model to generate a first denoised CT image; inputting the first de-noised CT image and a preset text into a multi-modal visual language large model to generate quality cues and detail cues about the first de-noised CT image; denoising the first denoised CT image again by using a trained generation diffusion model according to the quality cue words and the detail cue words to generate a second denoised CT image; inputting the second de-noised CT image serving as an initial de-noised CT image into a three-domain consistency iteration framework for fidelity, and obtaining a final de-noised CT image when an iteration stop condition is met; wherein the three domains comprise an image domain, a projection domain and a wavelet domain. The method can be conveniently applied to various noisy CT images by utilizing the emergence capability and the characterization capability of a large model and combining and designing a denoising frame.

The invention relates to an adaptive BM3D terahertz medical image denoising method based on KSVD and SSIM optimization, and belongs to the technical field of image processing. In the basic estimation stage, firstly, an 8 * 8 reference block is selected in a noise image, similar blocks are searched in a neighborhood with the reference block as the center to form a similar block group, and noise is removed and image details are recovered through hard filter value filtering processing. And finally, the BM3D algorithm performs weighted average processing on the estimation blocks in all the groups to generate a final de-noised image. And carrying out KSVD noise reduction on the image subjected to basic estimation, inputting the image subjected to noise reduction and an original noise image into final estimation, and carrying out block matching grouping, Wiener filtering and aggregation to form a final result. According to the method, local sparsity and non-local similarity of pictures can be fully utilized, cross-regional redundant information is utilized to suppress noise, the noise reduction capability is improved, meanwhile, more structural similarity is reserved, and a better effect is achieved in the face of high noise.

The invention discloses a photon counting CT image noise reduction method based on multi-channel 3D U-net, relates to the technical field of medical image processing, and aims to solve the problems of insufficient spatial information utilization of a traditional 2D CNN, complex calculation of a 3DCNN and poor noise reduction effect of a traditional method. According to the method, high-energy, low-energy and all-energy images obtained by photon counting CT are used as three-dimensional multi-channel input, and a matrix containing space and energy spectrum dimensions is formed after preprocessing. Multi-scale three-dimensional features are extracted through a 3D U-net encoder, deep semantic information is aggregated through a bottleneck layer, a decoder carries out deconvolution up-sampling and is fused with an encoder feature map through jump connection, and meanwhile, an attention module is embedded in the jump connection to generate an attention mask so as to strengthen key area features; a mixed loss function training network including mean square error loss and structural similarity loss is adopted, and pixel-level precision and structural retention are balanced.