The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions a...The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.展开更多
In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantl...In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.展开更多
To enable proper diagnosis of a patient,medical images must demonstrate no presence of noise and artifacts.The major hurdle lies in acquiring these images in such a manner that extraneous variables,causing distortions...To enable proper diagnosis of a patient,medical images must demonstrate no presence of noise and artifacts.The major hurdle lies in acquiring these images in such a manner that extraneous variables,causing distortions in the form of noise and artifacts,are kept to a bare minimum.The unexpected change realized during the acquisition process specifically attacks the integrity of the image’s quality,while indirectly attacking the effectiveness of the diagnostic process.It is thus crucial that this is attended to with maximum efficiency at the level of pertinent expertise.The solution to these challenges presents a complex dilemma at the acquisition stage,where image processing techniques must be adopted.The necessity of this mandatory image pre-processing step underpins the implementation of traditional state-of-the-art methods to create functional and robust denoising or recovery devices.This article hereby provides an extensive systematic review of the above techniques,with the purpose of presenting a systematic evaluation of their effect on medical images under three different distributions of noise,i.e.,Gaussian,Poisson,and Rician.A thorough analysis of these methods is conducted using eight evaluation parameters to highlight the unique features of each method.The covered denoising methods are essential in actual clinical scenarios where the preservation of anatomical details is crucial for accurate and safe diagnosis,such as tumor detection in MRI and vascular imaging in CT.展开更多
Imaging sonar devices generate sonar images by receiving echoes from objects,which are often accompanied by severe speckle noise,resulting in image distortion and information loss.Common optical denoising methods do n...Imaging sonar devices generate sonar images by receiving echoes from objects,which are often accompanied by severe speckle noise,resulting in image distortion and information loss.Common optical denoising methods do not work well in removing speckle noise from sonar images and may even reduce their visual quality.To address this issue,a sonar image denoising method based on fuzzy clustering and the undecimated dual-tree complex wavelet transform is proposed.This method provides a perfect translation invariance and an improved directional selectivity during image decomposition,leading to richer representation of noise and edges in high frequency coefficients.Fuzzy clustering can separate noise from useful information according to the amplitude characteristics of speckle noise,preserving the latter and achieving the goal of noise removal.Additionally,the low frequency coefficients are smoothed using bilateral filtering to improve the visual quality of the image.To verify the effectiveness of the algorithm,multiple groups of ablation experiments were conducted,and speckle sonar images with different variances were evaluated and compared with existing speckle removal methods in the transform domain.The experimental results show that the proposed method can effectively improve image quality,especially in cases of severe noise,where it still achieves a good denoising performance.展开更多
In wireless communication scenarios,especially in low-bandwidth or noisy transmission conditions,image data is often degraded by interference during acquisition or transmission.To address this,we proposed Wasserstein ...In wireless communication scenarios,especially in low-bandwidth or noisy transmission conditions,image data is often degraded by interference during acquisition or transmission.To address this,we proposed Wasserstein frequency generative adversarial networks(WF-GAN),a frequency-aware denoising model based on wavelet transformation.By decomposing images into four frequency sub-bands,the model separates low-frequency contour information from high-frequency texture details.Contour guidance is applied to preserve structural integrity,while adversarial training enhances texture fidelity in the high-frequency bands.A joint loss function,incorporating frequency-domain loss and perceptual loss,is designed to reduce detail degradation during denoising.Experiments on public image datasets,with Gaussian noise applied to simulate wireless communication interference,demonstrate that WF-GAN consistently outperforms both traditional and deep learning-based denoising methods in terms of visual quality and quantitative metrics.These results highlight its potential for robust image processing in wireless communication systems.展开更多
Terahertz imaging technology has great potential applications in areas,such as remote sensing,navigation,security checks,and so on.However,terahertz images usually have the problems of heavy noises and low resolution....Terahertz imaging technology has great potential applications in areas,such as remote sensing,navigation,security checks,and so on.However,terahertz images usually have the problems of heavy noises and low resolution.Previous terahertz image denoising methods are mainly based on traditional image processing methods,which have limited denoising effects on the terahertz noise.Existing deep learning-based image denoising methods are mostly used in natural images and easily cause a large amount of detail loss when denoising terahertz images.Here,a residual-learning-based multiscale hybridconvolution residual network(MHRNet)is proposed for terahertz image denoising,which can remove noises while preserving detail features in terahertz images.Specifically,a multiscale hybrid-convolution residual block(MHRB)is designed to extract rich detail features and local prediction residual noise from terahertz images.Specifically,MHRB is a residual structure composed of a multiscale dilated convolution block,a bottleneck layer,and a multiscale convolution block.MHRNet uses the MHRB and global residual learning to achieve terahertz image denoising.Ablation studies are performed to validate the effectiveness of MHRB.A series of experiments are conducted on the public terahertz image datasets.The experimental results demonstrate that MHRNet has an excellent denoising effect on synthetic and real noisy terahertz images.Compared with existing methods,MHRNet achieves comprehensive competitive results.展开更多
In order to decrease the sensitivity of the constant scale parameter, adaptively optimize the scale parameter in the iteration regularization model （IRM） and attain a desirable level of applicability for image denoi...In order to decrease the sensitivity of the constant scale parameter, adaptively optimize the scale parameter in the iteration regularization model （IRM） and attain a desirable level of applicability for image denoising, a novel IRM with the adaptive scale parameter is proposed. First, the classic regularization item is modified and the equation of the adaptive scale parameter is deduced. Then, the initial value of the varying scale parameter is obtained by the trend of the number of iterations and the scale parameter sequence vectors. Finally, the novel iterative regularization method is used for image denoising. Numerical experiments show that compared with the IRM with the constant scale parameter, the proposed method with the varying scale parameter can not only reduce the number of iterations when the scale parameter becomes smaller, but also efficiently remove noise when the scale parameter becomes bigger and well preserve the details of images.展开更多
Rather than attempting to separate signal from noise in the spatial domain, it is often advantageous to work in a transform domain. Building on previous work, a novel denoising method based on local adaptive least squ...Rather than attempting to separate signal from noise in the spatial domain, it is often advantageous to work in a transform domain. Building on previous work, a novel denoising method based on local adaptive least squares support vector regression is proposed. Investigation on real images contaminated by Gaussian noise has demonstrated that the proposed method can achieve an acceptable trade off between the noise removal and smoothing of the edges and details.展开更多
Classic non-local means (CNLM) algorithm uses the inherent self-similarity in images for noise removal. The denoised pixel value is estimated through the weighted average of all the pixels in its non-local neighborhoo...Classic non-local means (CNLM) algorithm uses the inherent self-similarity in images for noise removal. The denoised pixel value is estimated through the weighted average of all the pixels in its non-local neighborhood. In the CNLM algorithm, the differences between the pixel value and the distance of the pixel to the center are both taken into consideration to calculate the weighting coefficients. However, the Gaussian kernel cannot reflect the information of edge and structure due to its isotropy, and it has poor performance in flat regions. In this paper, an improved non-local means algorithm based on local edge direction is presented for image denoising. In edge and structure regions, the steering kernel regression (SKR) coefficients are used to calculate the weights, and in flat regions the average kernel is used. Experiments show that the proposed algorithm can effectively protect edge and structure while removing noises better when compared with the CNLM algorithm.展开更多
In order to preferably identify infrared image of refuge chamber, reduce image noises of refuge chamber and retain more image details, we propose the method of combining two-dimensional discrete wavelet transform and ...In order to preferably identify infrared image of refuge chamber, reduce image noises of refuge chamber and retain more image details, we propose the method of combining two-dimensional discrete wavelet transform and bilateral denoising. First, the wavelet transform is adopted to decompose the image of refuge chamber, of which low frequency component remains unchanged. Then, three high-frequency components are treated by bilateral filtering, and the image is reconstructed. The result shows that the combination of bilateral filtering and wavelet transform for image denoising can better retain the details which are included in the image, while providing better visual effect. This is superior to using either bilateral filtering or wavelet transform alone. It is useful for perfecting emergency refuge system of coal mines.展开更多
Graph filtering,which is founded on the theory of graph signal processing,is proved as a useful tool for image denoising.Most graph filtering methods focus on learning an ideal lowpass filter to remove noise,where cle...Graph filtering,which is founded on the theory of graph signal processing,is proved as a useful tool for image denoising.Most graph filtering methods focus on learning an ideal lowpass filter to remove noise,where clean images are restored from noisy ones by retaining the image components in low graph frequency bands.However,this lowpass filter has limited ability to separate the low-frequency noise from clean images such that it makes the denoising procedure less effective.To address this issue,we propose an adaptive weighted graph filtering(AWGF)method to replace the design of traditional ideal lowpass filter.In detail,we reassess the existing low-rank denoising method with adaptive regularizer learning(ARLLR)from the view of graph filtering.A shrinkage approach subsequently is presented on the graph frequency domain,where the components of noisy image are adaptively decreased in each band by calculating their component significances.As a result,it makes the proposed graph filtering more explainable and suitable for denoising.Meanwhile,we demonstrate a graph filter under the constraint of subspace representation is employed in the ARLLR method.Therefore,ARLLR can be treated as a special form of graph filtering.It not only enriches the theory of graph filtering,but also builds a bridge from the low-rank methods to the graph filtering methods.In the experiments,we perform the AWGF method with a graph filter generated by the classical graph Laplacian matrix.The results show our method can achieve a comparable denoising performance with several state-of-the-art denoising methods.展开更多
An adaptive image denosing technique was proposed to achieve the tradeoff between details retain and noises removal. In order to achieve this objective, the contourlet transform was introduced and a new threshold meth...An adaptive image denosing technique was proposed to achieve the tradeoff between details retain and noises removal. In order to achieve this objective, the contourlet transform was introduced and a new threshold method, namely CWinShrink, is presented. It shrinks the contourlet coefficients with adaptive shrinkage factors. The shrinkage factors were calculated with reference to the sum of squares of the contourlet coefficients within the neighborhood window. This approach achieves enhanced results for images those are corrupted with additive Gaussian noise. In numerical comparisons with various methods, for a set of noisy images （the PSNR range fi＇om 10.86dB to 26.91dB） , the presented method outperforms VisuShrink and Wiener filter in terms of the PSNR. Experiments also show that this method not only keeps the details of image but also yields denoised images with better visual quality.展开更多
In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the co...In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the convergence efficiency, thegiven method introduces the gradient penalty term to WGAN network. The novelperceptual loss is introduced to make the texture information of the low-dose imagessensitive to the diagnostician eye. The experimental results show that compared with thestate-of-art methods, the time complexity is reduced, and the visual quality of low-doseCT images is significantly improved.展开更多
Sparse representation models have been shown promising results for image denoising. However, conventional sparse representation-based models cannot obtain satisfactory estimations for sparse coefficients and the dicti...Sparse representation models have been shown promising results for image denoising. However, conventional sparse representation-based models cannot obtain satisfactory estimations for sparse coefficients and the dictionary. To address this weakness, in this paper, we propose a novel fractional-order sparse representation(FSR) model. Specifically, we cluster the image patches into K groups, and calculate the singular values for each clean/noisy patch pair in the wavelet domain. Then the uniform fractional-order parameters are learned for each cluster.Then a novel fractional-order sample space is constructed using adaptive fractional-order parameters in the wavelet domain to obtain more accurate sparse coefficients and dictionary for image denoising. Extensive experimental results show that the proposed model outperforms state-of-the-art sparse representation-based models and the block-matching and 3D filtering algorithm in terms of denoising performance and the computational efficiency.展开更多
In order to improve the adaptiveness of TV/L2-based image denoising algorithm in differ- ent signal-to-noise ratio （SNR） environments, an iterative denoising method with automatic parame- ter selection is proposed. ...In order to improve the adaptiveness of TV/L2-based image denoising algorithm in differ- ent signal-to-noise ratio （SNR） environments, an iterative denoising method with automatic parame- ter selection is proposed. Based upon the close connection between optimization function of denois- ing problem and regularization parameter, an updating model is built to select the regularized param- eter. Both the parameter and the objective function are dynamically updated in alternating minimiza- tion iterations, consequently, it can make the algorithm work in different SNR environments. Mean- while, a strategy for choosing the initial regularization parameter is presented. Considering Morozov discrepancy principle, a convex function with respect to the regularization parameter is modeled. Via the optimization method, it is easy and fast to find the convergence value of parameter, which is suitable for the iterative image denoising algorithm. Comparing with several state-of-the-art algo- rithms, many experiments confirm that the denoising algorithm with the proposed parameter selec- tion is highly effective to evaluate peak signal-to-noise ratio （PSNR） and structural similarity展开更多
The increasing use of images in miscellaneous applications such as medical image analysis and visual quality inspection has led to growing interest in image processing.However,images are often contaminated with noise ...The increasing use of images in miscellaneous applications such as medical image analysis and visual quality inspection has led to growing interest in image processing.However,images are often contaminated with noise which may corrupt any of the following image processing steps.Therefore,noise filtering is often a necessary preprocessing step for the most image processing applications.Thus,in this paper an optimized field-programmable gate array(FPGA)design is proposed to implement the adaptive vector directional distance filter(AVDDF)in hardware/software(HW/SW)codesign context for removing noise from the images in real-time.For that,the high-level synthesis(HLS)flow is used through the Xilinx Vivado HLS tool to reduce the design complexity of the HW part.The SW part is developed based on C/C++programming language and executed on an advanced reduced instruction set computer(RISC)machines(ARM)Cortex-A53 processor.The communication between the SW and HW parts is achieved using the advanced extensible Interface stream(AXI-stream)interface to increase the data bandwidth.The experiment results on the Xilinx ZCU102 FPGA board show an improvement in processing time of the AVDDF filter by 98%for the HW/SW implementation relative to the SW implementation.This result is given for the same quality of image between the HW/SW and SW implementations in terms of the normalized color difference(NCD)and the peak signal to noise ratio(PSNR).展开更多
With the explosion in the number of digital images taken every day,the demand for more accurate and visually pleasing images is increasing.However,the images captured by modern cameras are inevitably degraded by noise...With the explosion in the number of digital images taken every day,the demand for more accurate and visually pleasing images is increasing.However,the images captured by modern cameras are inevitably degraded by noise,which leads to deteriorated visual image quality.Therefore,work is required to reduce noise without losing image features(edges,corners,and other sharp structures).So far,researchers have already proposed various methods for decreasing noise.Each method has its own advantages and disadvantages.In this paper,we summarize some important research in the field of image denoising.First,we give the formulation of the image denoising problem,and then we present several image denoising techniques.In addition,we discuss the characteristics of these techniques.Finally,we provide several promising directions for future research.展开更多
Deep convolutional neural networks(CNNs)with strong learning abilities have been used in the field of image denoising.However,some CNNs depend on a single deep network to train an image denoising model,which will have...Deep convolutional neural networks(CNNs)with strong learning abilities have been used in the field of image denoising.However,some CNNs depend on a single deep network to train an image denoising model,which will have poor performance in complex screens.To address this problem,we propose a hybrid denoising CNN(HDCNN).HDCNN is composed of a dilated block(DB),RepVGG block(RVB),feature refinement block(FB),and a single convolution.DB combines a dilated convolution,batch normalization(BN),common convolutions,and activation function of ReLU to obtain more context information.RVB uses parallel combination of convolution,BN,and ReLU to extract complementary width features.FB is used to obtain more accurate information via refining obtained feature from the RVB.A single convolution collaborates a residual learning operation to construct a clean image.These key components make the HDCNN have good performance in image denoising.Experiment shows that the proposed HDCNN enjoys good denoising effect in public data sets.展开更多
In recent years,image restoration has become a huge subject,and finite hybrid model has been widely used in image denoising because of its easy modeling and strong explanatory results.The gaussian mixture model is the...In recent years,image restoration has become a huge subject,and finite hybrid model has been widely used in image denoising because of its easy modeling and strong explanatory results.The gaussian mixture model is the most common one.The existing image denoising methods usually assume that each component of the natural image is subject to the gaussian mixture model(GMM).However,this approach is not entirely reasonable.It is well known that most natural images are complex and their distribution is not entirely gaussian.As a result,there are still many problems that GMM cannot solve.This paper tries to improve the finite mixture model and introduces the asymmetric gaussian mixture model into it.Since the asymmetric gaussian mixture model can simulate the asymmetric distribution on the basis of the gaussian mixture model,it is more consistent with the natural image data,so the denoising effect of the natural complex image is better.We carried out image denoising experiments under different noise scales and types,and found that the asymmetric gaussian mixture model has better denoising effect and performance.展开更多
Image denoising is the basic problem of image processing. Quaternion wavelet transform is a new kind of multiresolution analysis tools. Image via quaternion wavelet transform, wavelet coefficients both in intrascale a...Image denoising is the basic problem of image processing. Quaternion wavelet transform is a new kind of multiresolution analysis tools. Image via quaternion wavelet transform, wavelet coefficients both in intrascale and in interscale have certain correla- tions. First, according to the correlation of quaternion wavelet coefficients in interscale, non-Ganssian distribution model is used to model its correlations, and the coefficients are divided into important and unimportance coefficients. Then we use the non-Gaussian distribution model to model the important coefficients and its adjacent coefficients, and utilize the MAP method estimate original image wavelet coefficients from noisy coefficients, so as to achieve the purpose of denoising. Experimental results show that our al- gorithm outperforms the other classical algorithms in peak signal-to-noise ratio and visual quality.展开更多
基金supported by the National Natural Science(No.U19A2063)the Jilin Provincial Development Program of Science and Technology (No.20230201080GX)the Jilin Province Education Department Scientific Research Project (No.JJKH20230851KJ)。
文摘The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.
文摘In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.
文摘To enable proper diagnosis of a patient,medical images must demonstrate no presence of noise and artifacts.The major hurdle lies in acquiring these images in such a manner that extraneous variables,causing distortions in the form of noise and artifacts,are kept to a bare minimum.The unexpected change realized during the acquisition process specifically attacks the integrity of the image’s quality,while indirectly attacking the effectiveness of the diagnostic process.It is thus crucial that this is attended to with maximum efficiency at the level of pertinent expertise.The solution to these challenges presents a complex dilemma at the acquisition stage,where image processing techniques must be adopted.The necessity of this mandatory image pre-processing step underpins the implementation of traditional state-of-the-art methods to create functional and robust denoising or recovery devices.This article hereby provides an extensive systematic review of the above techniques,with the purpose of presenting a systematic evaluation of their effect on medical images under three different distributions of noise,i.e.,Gaussian,Poisson,and Rician.A thorough analysis of these methods is conducted using eight evaluation parameters to highlight the unique features of each method.The covered denoising methods are essential in actual clinical scenarios where the preservation of anatomical details is crucial for accurate and safe diagnosis,such as tumor detection in MRI and vascular imaging in CT.
基金the National Natural Science Foundation of China(No.62065001)the Yunnan Young and Middle-aged Academic and Technical Leaders Reserve Talent Project(No.202205AC160001)+1 种基金the Science and Technology Programs of Yunnan Provincial Science and Technology Department(No.202101BA070001-054)the Special Basic Cooperative Research Programs of Yunnan Provincial Undergraduate Universities Association(No.2019FH001(-066))。
文摘Imaging sonar devices generate sonar images by receiving echoes from objects,which are often accompanied by severe speckle noise,resulting in image distortion and information loss.Common optical denoising methods do not work well in removing speckle noise from sonar images and may even reduce their visual quality.To address this issue,a sonar image denoising method based on fuzzy clustering and the undecimated dual-tree complex wavelet transform is proposed.This method provides a perfect translation invariance and an improved directional selectivity during image decomposition,leading to richer representation of noise and edges in high frequency coefficients.Fuzzy clustering can separate noise from useful information according to the amplitude characteristics of speckle noise,preserving the latter and achieving the goal of noise removal.Additionally,the low frequency coefficients are smoothed using bilateral filtering to improve the visual quality of the image.To verify the effectiveness of the algorithm,multiple groups of ablation experiments were conducted,and speckle sonar images with different variances were evaluated and compared with existing speckle removal methods in the transform domain.The experimental results show that the proposed method can effectively improve image quality,especially in cases of severe noise,where it still achieves a good denoising performance.
基金supported in part by the Beijing Natural Science Foundation(No.4254072).
文摘In wireless communication scenarios,especially in low-bandwidth or noisy transmission conditions,image data is often degraded by interference during acquisition or transmission.To address this,we proposed Wasserstein frequency generative adversarial networks(WF-GAN),a frequency-aware denoising model based on wavelet transformation.By decomposing images into four frequency sub-bands,the model separates low-frequency contour information from high-frequency texture details.Contour guidance is applied to preserve structural integrity,while adversarial training enhances texture fidelity in the high-frequency bands.A joint loss function,incorporating frequency-domain loss and perceptual loss,is designed to reduce detail degradation during denoising.Experiments on public image datasets,with Gaussian noise applied to simulate wireless communication interference,demonstrate that WF-GAN consistently outperforms both traditional and deep learning-based denoising methods in terms of visual quality and quantitative metrics.These results highlight its potential for robust image processing in wireless communication systems.
基金National Natural Science Foundation of China,Grant/Award Number:62173098,62104047Guangdong Provincial Key Laboratory of Cyber-Physical System,Grant/Award Number:2020B1212060069。
文摘Terahertz imaging technology has great potential applications in areas,such as remote sensing,navigation,security checks,and so on.However,terahertz images usually have the problems of heavy noises and low resolution.Previous terahertz image denoising methods are mainly based on traditional image processing methods,which have limited denoising effects on the terahertz noise.Existing deep learning-based image denoising methods are mostly used in natural images and easily cause a large amount of detail loss when denoising terahertz images.Here,a residual-learning-based multiscale hybridconvolution residual network(MHRNet)is proposed for terahertz image denoising,which can remove noises while preserving detail features in terahertz images.Specifically,a multiscale hybrid-convolution residual block(MHRB)is designed to extract rich detail features and local prediction residual noise from terahertz images.Specifically,MHRB is a residual structure composed of a multiscale dilated convolution block,a bottleneck layer,and a multiscale convolution block.MHRNet uses the MHRB and global residual learning to achieve terahertz image denoising.Ablation studies are performed to validate the effectiveness of MHRB.A series of experiments are conducted on the public terahertz image datasets.The experimental results demonstrate that MHRNet has an excellent denoising effect on synthetic and real noisy terahertz images.Compared with existing methods,MHRNet achieves comprehensive competitive results.
基金The National Natural Science Foundation of China(No.60702069)the Research Project of Department of Education of Zhe-jiang Province (No.20060601)+1 种基金the Natural Science Foundation of Zhe-jiang Province (No.Y1080851)Shanghai International Cooperation onRegion of France (No.06SR07109)
文摘In order to decrease the sensitivity of the constant scale parameter, adaptively optimize the scale parameter in the iteration regularization model （IRM） and attain a desirable level of applicability for image denoising, a novel IRM with the adaptive scale parameter is proposed. First, the classic regularization item is modified and the equation of the adaptive scale parameter is deduced. Then, the initial value of the varying scale parameter is obtained by the trend of the number of iterations and the scale parameter sequence vectors. Finally, the novel iterative regularization method is used for image denoising. Numerical experiments show that compared with the IRM with the constant scale parameter, the proposed method with the varying scale parameter can not only reduce the number of iterations when the scale parameter becomes smaller, but also efficiently remove noise when the scale parameter becomes bigger and well preserve the details of images.
基金Supported by the Foundation of Hubei Provincial Department of Education(No.2003EB0018).
文摘Rather than attempting to separate signal from noise in the spatial domain, it is often advantageous to work in a transform domain. Building on previous work, a novel denoising method based on local adaptive least squares support vector regression is proposed. Investigation on real images contaminated by Gaussian noise has demonstrated that the proposed method can achieve an acceptable trade off between the noise removal and smoothing of the edges and details.
基金National Key Research and Development Program of China(No.2016YFC0101601)Fund for Shanxi“1331 Project”Key Innovative Research Team+1 种基金Shanxi Province Science Foundation for Youths(No.201601D021080)Universities Science and Technology Innovation Project of Shanxi Province(No.2017107)
文摘Classic non-local means (CNLM) algorithm uses the inherent self-similarity in images for noise removal. The denoised pixel value is estimated through the weighted average of all the pixels in its non-local neighborhood. In the CNLM algorithm, the differences between the pixel value and the distance of the pixel to the center are both taken into consideration to calculate the weighting coefficients. However, the Gaussian kernel cannot reflect the information of edge and structure due to its isotropy, and it has poor performance in flat regions. In this paper, an improved non-local means algorithm based on local edge direction is presented for image denoising. In edge and structure regions, the steering kernel regression (SKR) coefficients are used to calculate the weights, and in flat regions the average kernel is used. Experiments show that the proposed algorithm can effectively protect edge and structure while removing noises better when compared with the CNLM algorithm.
基金the Scientific Research Project of Zhejiang Education Department of China (No. Y20108569)the Soft Science Project of Ningbo of China (No. 2011A1058)the Soft Science of Zhejiang Association for Science and Technology of China (No. KX12E-10)
文摘In order to preferably identify infrared image of refuge chamber, reduce image noises of refuge chamber and retain more image details, we propose the method of combining two-dimensional discrete wavelet transform and bilateral denoising. First, the wavelet transform is adopted to decompose the image of refuge chamber, of which low frequency component remains unchanged. Then, three high-frequency components are treated by bilateral filtering, and the image is reconstructed. The result shows that the combination of bilateral filtering and wavelet transform for image denoising can better retain the details which are included in the image, while providing better visual effect. This is superior to using either bilateral filtering or wavelet transform alone. It is useful for perfecting emergency refuge system of coal mines.
基金This work is supported by National Natural Science Foundation of China[61673108,41706103]The initials of authors who received these grants are LZ and YZ,respectively.It is also supported by Natural Science Foundation of Jiangsu Province,China[BK20170306]The initials of author who received this grant are YZ.
文摘Graph filtering,which is founded on the theory of graph signal processing,is proved as a useful tool for image denoising.Most graph filtering methods focus on learning an ideal lowpass filter to remove noise,where clean images are restored from noisy ones by retaining the image components in low graph frequency bands.However,this lowpass filter has limited ability to separate the low-frequency noise from clean images such that it makes the denoising procedure less effective.To address this issue,we propose an adaptive weighted graph filtering(AWGF)method to replace the design of traditional ideal lowpass filter.In detail,we reassess the existing low-rank denoising method with adaptive regularizer learning(ARLLR)from the view of graph filtering.A shrinkage approach subsequently is presented on the graph frequency domain,where the components of noisy image are adaptively decreased in each band by calculating their component significances.As a result,it makes the proposed graph filtering more explainable and suitable for denoising.Meanwhile,we demonstrate a graph filter under the constraint of subspace representation is employed in the ARLLR method.Therefore,ARLLR can be treated as a special form of graph filtering.It not only enriches the theory of graph filtering,but also builds a bridge from the low-rank methods to the graph filtering methods.In the experiments,we perform the AWGF method with a graph filter generated by the classical graph Laplacian matrix.The results show our method can achieve a comparable denoising performance with several state-of-the-art denoising methods.
基金Sponsored by Key Lab of Optoelectronic Technology &System,Department of Education, China(Grant No.200373 -1 -2).
文摘An adaptive image denosing technique was proposed to achieve the tradeoff between details retain and noises removal. In order to achieve this objective, the contourlet transform was introduced and a new threshold method, namely CWinShrink, is presented. It shrinks the contourlet coefficients with adaptive shrinkage factors. The shrinkage factors were calculated with reference to the sum of squares of the contourlet coefficients within the neighborhood window. This approach achieves enhanced results for images those are corrupted with additive Gaussian noise. In numerical comparisons with various methods, for a set of noisy images （the PSNR range fi＇om 10.86dB to 26.91dB） , the presented method outperforms VisuShrink and Wiener filter in terms of the PSNR. Experiments also show that this method not only keeps the details of image but also yields denoised images with better visual quality.
基金supported by National Natural Science Foundation ofChina (61672279)Project of “Six Talents Peak” in Jiangsu (2012-WLW-023)OpenFoundation of State Key Laboratory of Hydrology-Water Resources and HydraulicEngineering, Nanjing Hydraulic Research Institute, China (2016491411).
文摘In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the convergence efficiency, thegiven method introduces the gradient penalty term to WGAN network. The novelperceptual loss is introduced to make the texture information of the low-dose imagessensitive to the diagnostician eye. The experimental results show that compared with thestate-of-art methods, the time complexity is reduced, and the visual quality of low-doseCT images is significantly improved.
基金supported by the National Natural Science Foundation of China(61573219,61402203,61401209,61701192,61671274)the Opening Fund of Shandong Provincial Key Laboratory of Network Based Intelligent Computing+2 种基金the Fostering Project of Dominant DisciplineTalent Team of Shandong Province Higher Education InstitutionsFostering Project of Dominant Discipline and Talent Team of SDUFE
文摘Sparse representation models have been shown promising results for image denoising. However, conventional sparse representation-based models cannot obtain satisfactory estimations for sparse coefficients and the dictionary. To address this weakness, in this paper, we propose a novel fractional-order sparse representation(FSR) model. Specifically, we cluster the image patches into K groups, and calculate the singular values for each clean/noisy patch pair in the wavelet domain. Then the uniform fractional-order parameters are learned for each cluster.Then a novel fractional-order sample space is constructed using adaptive fractional-order parameters in the wavelet domain to obtain more accurate sparse coefficients and dictionary for image denoising. Extensive experimental results show that the proposed model outperforms state-of-the-art sparse representation-based models and the block-matching and 3D filtering algorithm in terms of denoising performance and the computational efficiency.
基金Supported by the National High Technology Research and Development Program of China(863Program)(2012AA8012011C)
文摘In order to improve the adaptiveness of TV/L2-based image denoising algorithm in differ- ent signal-to-noise ratio （SNR） environments, an iterative denoising method with automatic parame- ter selection is proposed. Based upon the close connection between optimization function of denois- ing problem and regularization parameter, an updating model is built to select the regularized param- eter. Both the parameter and the objective function are dynamically updated in alternating minimiza- tion iterations, consequently, it can make the algorithm work in different SNR environments. Mean- while, a strategy for choosing the initial regularization parameter is presented. Considering Morozov discrepancy principle, a convex function with respect to the regularization parameter is modeled. Via the optimization method, it is easy and fast to find the convergence value of parameter, which is suitable for the iterative image denoising algorithm. Comparing with several state-of-the-art algo- rithms, many experiments confirm that the denoising algorithm with the proposed parameter selec- tion is highly effective to evaluate peak signal-to-noise ratio （PSNR） and structural similarity
基金funded by the Deanship of Scientific Research at Jouf University(Kingdom of Saudi Arabia)under Grant No.DSR-2021-02-03106.
文摘The increasing use of images in miscellaneous applications such as medical image analysis and visual quality inspection has led to growing interest in image processing.However,images are often contaminated with noise which may corrupt any of the following image processing steps.Therefore,noise filtering is often a necessary preprocessing step for the most image processing applications.Thus,in this paper an optimized field-programmable gate array(FPGA)design is proposed to implement the adaptive vector directional distance filter(AVDDF)in hardware/software(HW/SW)codesign context for removing noise from the images in real-time.For that,the high-level synthesis(HLS)flow is used through the Xilinx Vivado HLS tool to reduce the design complexity of the HW part.The SW part is developed based on C/C++programming language and executed on an advanced reduced instruction set computer(RISC)machines(ARM)Cortex-A53 processor.The communication between the SW and HW parts is achieved using the advanced extensible Interface stream(AXI-stream)interface to increase the data bandwidth.The experiment results on the Xilinx ZCU102 FPGA board show an improvement in processing time of the AVDDF filter by 98%for the HW/SW implementation relative to the SW implementation.This result is given for the same quality of image between the HW/SW and SW implementations in terms of the normalized color difference(NCD)and the peak signal to noise ratio(PSNR).
基金This work is supported by NSFC Joint Fund with Zhejiang Integration of Informatization and Industrialization under Key Project(No.U1609218)the National Nature Science Foundation of China(No.61602277)Shandong Provincial Natural Science Foundation of China(No.ZR2016FQ12).
文摘With the explosion in the number of digital images taken every day,the demand for more accurate and visually pleasing images is increasing.However,the images captured by modern cameras are inevitably degraded by noise,which leads to deteriorated visual image quality.Therefore,work is required to reduce noise without losing image features(edges,corners,and other sharp structures).So far,researchers have already proposed various methods for decreasing noise.Each method has its own advantages and disadvantages.In this paper,we summarize some important research in the field of image denoising.First,we give the formulation of the image denoising problem,and then we present several image denoising techniques.In addition,we discuss the characteristics of these techniques.Finally,we provide several promising directions for future research.
基金supported in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515110079in part by the Fundamental Research Funds for the Central Universities under Grant D5000210966in part by the Basic Research Plan in Taicang under Grant TC2021JC23.
文摘Deep convolutional neural networks(CNNs)with strong learning abilities have been used in the field of image denoising.However,some CNNs depend on a single deep network to train an image denoising model,which will have poor performance in complex screens.To address this problem,we propose a hybrid denoising CNN(HDCNN).HDCNN is composed of a dilated block(DB),RepVGG block(RVB),feature refinement block(FB),and a single convolution.DB combines a dilated convolution,batch normalization(BN),common convolutions,and activation function of ReLU to obtain more context information.RVB uses parallel combination of convolution,BN,and ReLU to extract complementary width features.FB is used to obtain more accurate information via refining obtained feature from the RVB.A single convolution collaborates a residual learning operation to construct a clean image.These key components make the HDCNN have good performance in image denoising.Experiment shows that the proposed HDCNN enjoys good denoising effect in public data sets.
基金This work was partly supported by the National Natural Science Foundation of China under Grants 61672293.
文摘In recent years,image restoration has become a huge subject,and finite hybrid model has been widely used in image denoising because of its easy modeling and strong explanatory results.The gaussian mixture model is the most common one.The existing image denoising methods usually assume that each component of the natural image is subject to the gaussian mixture model(GMM).However,this approach is not entirely reasonable.It is well known that most natural images are complex and their distribution is not entirely gaussian.As a result,there are still many problems that GMM cannot solve.This paper tries to improve the finite mixture model and introduces the asymmetric gaussian mixture model into it.Since the asymmetric gaussian mixture model can simulate the asymmetric distribution on the basis of the gaussian mixture model,it is more consistent with the natural image data,so the denoising effect of the natural complex image is better.We carried out image denoising experiments under different noise scales and types,and found that the asymmetric gaussian mixture model has better denoising effect and performance.
基金Supported by Natural Science Foundation of Anhui (No.11040606M06)
文摘Image denoising is the basic problem of image processing. Quaternion wavelet transform is a new kind of multiresolution analysis tools. Image via quaternion wavelet transform, wavelet coefficients both in intrascale and in interscale have certain correla- tions. First, according to the correlation of quaternion wavelet coefficients in interscale, non-Ganssian distribution model is used to model its correlations, and the coefficients are divided into important and unimportance coefficients. Then we use the non-Gaussian distribution model to model the important coefficients and its adjacent coefficients, and utilize the MAP method estimate original image wavelet coefficients from noisy coefficients, so as to achieve the purpose of denoising. Experimental results show that our al- gorithm outperforms the other classical algorithms in peak signal-to-noise ratio and visual quality.
