The internal energy distribution of waves can be described using ocean-wave spectra.In many ways,obtaining wave spectra on a global scale is critical.Surface waves investigation and monitoring onboard the Chinese-Fren...The internal energy distribution of waves can be described using ocean-wave spectra.In many ways,obtaining wave spectra on a global scale is critical.Surface waves investigation and monitoring onboard the Chinese-French oceanography satellite is the first space-borne instrument for detecting wave spectra specially,which was launched on October 29,2018.It can avoid the shortage of synthetic aperture radar detection results while still having some problems,especially with the effects of speckle noise.In this study,a method to suppress the speckle noise is proposed.First,the empirical formula for background speckle noise is established.Second,many spatio-temporal representative fluctuation spectra are classified and averaged.Third,rational transfer function filtering is used to obtain speckle noise close to the along-track direction.Finally,a signal-to-noise ratio threshold is used to suppress the abnormal speckle noise.This method solves the problems existing in previous denoising methods,such as excessive denoising in the along-track direction and the inability of some abnormal noises to be denoised in the two-dimensional directional wave spectra.展开更多
In spite of the advancement in computerized imaging,many image modalities produce images with commotion influencing both the visual quality and upsetting quantitative image analysis.In this way,the research in the zone...In spite of the advancement in computerized imaging,many image modalities produce images with commotion influencing both the visual quality and upsetting quantitative image analysis.In this way,the research in the zone of image denoising is very dynamic.Among an extraordinary assortment of image restoration and denoising techniques the neural network system-based noise sup-pression is a basic and productive methodology.In this paper,Bilateral Filter(BF)based Modular Neural Networks(MNN)has been utilized for speckle noise sup-pression in the ultrasound image.Initial step the BFfilter is used tofilter the input image.From the output of BF,statistical features such as mean,standard devia-tion,median and kurtosis have been extracted and these features are used to train the MNN.Then,thefiltered images from the BF are again denoised using MNN.The ultrasound dataset from the Kaggle site is used for the training and testing process.The simulation outcomes demonstrate that the BF-MNNfiltering method performs better for the multiplicative noise concealment in UltraSound(US)images.From the simulation results,it has been observed that BF-MNN performs better than the existing techniques in terms of peak signal to noise ratio(34.89),Structural Similarity Index(0.89)and Edge Preservation Index(0.67).展开更多
An integrated method based on optical and digital image processing is presented to suppress speckle in digital holography. A spatial light modulator is adopted to introduce random phases to the illuminating beam. Mult...An integrated method based on optical and digital image processing is presented to suppress speckle in digital holography. A spatial light modulator is adopted to introduce random phases to the illuminating beam. Multiple holograms are reconstructed and superimposed, and the intensity is averaged to smooth the noise. The adaptive algorithm based on the nonlocal means is designed to further suppress the speckle. The presented method is compared with other methods reduction is improved, and the proposed method is effective The experimental results show that speckle and feasible.展开更多
Ultrasound is a low-cost,non-invasive and real-time imaging modality that has proved popular for many medical applications.Unfortunately,the acquired ultrasound images are often corrupted by speckle noise from scatter...Ultrasound is a low-cost,non-invasive and real-time imaging modality that has proved popular for many medical applications.Unfortunately,the acquired ultrasound images are often corrupted by speckle noise from scatterers smaller than ultrasound beam wavelength.The signal-dependent speckle noise makes visual observation difficult.In this paper,we propose a patch-based low-rank approach for reducing the speckle noise in ultrasound images.After constructing the patch group of the ultrasound images by the block-matching scheme,we establish a variational model using the weighted nuclear norm as a regularizer for the patch group.The alternating direction method of multipliers(ADMM)is applied for solving the established nonconvex model.We return all the approximate patches to their original locations and get the final restored ultrasound images.Experimental results are given to demonstrate that the proposed method outperforms some existing state-of-the-art methods in terms of visual quality and quantitative measures.展开更多
This article introduces a fastmeshless algorithm for the numerical solution nonlinear partial differential equations(PDE)by Radial Basis Functions(RBFs)approximation connected with the Total Variation(TV)-basedminimiz...This article introduces a fastmeshless algorithm for the numerical solution nonlinear partial differential equations(PDE)by Radial Basis Functions(RBFs)approximation connected with the Total Variation(TV)-basedminimization functional and to show its application to image denoising containing multiplicative noise.These capabilities used within the proposed algorithm have not only the quality of image denoising,edge preservation but also the property of minimization of staircase effect which results in blocky effects in the images.It is worth mentioning that the recommended method can be easily employed for nonlinear problems due to the lack of dependence on a mesh or integration procedure.The numerical investigations and corresponding examples prove the effectiveness of the recommended algorithm regarding the robustness and visual improvement as well as peak-signal-to-noise ratio(PSNR),signal-to-noise ratio(SNR),and structural similarity index(SSIM)corresponded to the current conventional TV-based schemes.展开更多
Synthetic aperture radar （SAR） images are corrupted by multiplicative speckle noise which limits the performance of the classical coder/decoder algorithm in spatial domain. The relatively new transform of multiwavel...Synthetic aperture radar （SAR） images are corrupted by multiplicative speckle noise which limits the performance of the classical coder/decoder algorithm in spatial domain. The relatively new transform of multiwavelets can possess desirable features simultaneously, such as orthogonality and symmetry, while scalar wavelets cannot. In this paper we propose a compression scheme combining with speckle noise reduction within the multiwavelet framework. Compared with classical set partitioning in hierarchical trees （SPIHT） algorithm, our method achieves favorable peak signal to noise ratio （PSNR） and superior speckle noise reduction performances.展开更多
This paper proposes a speckle-suppression method for ocean internal solitary wave（ISW） synthetic aperture radar（SAR） images by using the curvelet transform.The band-shaped signatures of ocean ISWs in SAR images sh...This paper proposes a speckle-suppression method for ocean internal solitary wave（ISW） synthetic aperture radar（SAR） images by using the curvelet transform.The band-shaped signatures of ocean ISWs in SAR images show obvious scale and directional characteristics.The curvelet transform possesses a very high scale and directional sensitivity.Therefore,the curvelet transform is very efficient in analyzing wave signals in SAR images.A noisy ocean ISW SAR image can be decomposed at different scales,directions,and positions using the curvelet transform.The information of the ISWs is centralized in the curvelet coefficients of certain directions under certain scales,whereas the speckle noise is distributed in every scale and direction.By manipulating the curvelet coefficients,the signals of the ISWs can be extracted from the noisy SAR image.Finally,the speckle noise is suppressed and the ISW feature is enhanced by adding the signals of the ISWs back to the original SAR image.Experiments demonstrate the effectiveness of this method.展开更多
Computer vision is one of the significant trends in computer science.It plays as a vital role in many applications,especially in the medical field.Early detection and segmentation of different tumors is a big challeng...Computer vision is one of the significant trends in computer science.It plays as a vital role in many applications,especially in the medical field.Early detection and segmentation of different tumors is a big challenge in the medical world.The proposed framework uses ultrasound images from Kaggle,applying five diverse models to denoise the images,using the best possible noise-free image as input to the U-Net model for segmentation of the tumor,and then using the Convolution Neural Network(CNN)model to classify whether the tumor is benign,malignant,or normal.The main challenge faced by the framework in the segmentation is the speckle noise.It’s is a multiplicative and negative issue in breast ultrasound imaging,because of this noise,the image resolution and contrast become reduced,which affects the diagnostic value of this imaging modality.As result,speckle noise reduction is very vital for the segmentation process.The framework uses five models such as Generative Adversarial Denoising Network(DGAN-Net),Denoising U-Shaped Net(D-U-NET),Batch Renormalization U-Net(Br-UNET),Generative Adversarial Network(GAN),and Nonlocal Neutrosophic ofWiener Filtering(NLNWF)for reducing the speckle noise from the breast ultrasound images then choose the best image according to peak signal to noise ratio(PSNR)for each level of speckle-noise.The five used methods have been compared with classical filters such as Bilateral,Frost,Kuan,and Lee and they proved their efficiency according to PSNR in different levels of noise.The five diverse models are achieved PSNR results for speckle noise at level(0.1,0.25,0.5,0.75),(33.354,29.415,27.218,24.115),(31.424,28.353,27.246,24.244),(32.243,28.42,27.744,24.893),(31.234,28.212,26.983,23.234)and(33.013,29.491,28.556,25.011)forDGAN,Br-U-NET,D-U-NET,GANand NLNWF respectively.According to the value of PSNR and level of speckle noise,the best image passed for segmentation using U-Net and classification usingCNNto detect tumor type.The experiments proved the quality ofU-Net and CNN in segmentation and classification respectively,since they achieved 95.11 and 95.13 in segmentation and 95.55 and 95.67 in classification as dice score and accuracy respectively.展开更多
Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion detection.This paper presents a novel framework for segmenting the skin.The framework ...Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion detection.This paper presents a novel framework for segmenting the skin.The framework contains two main stages:The first stage is for removing different types of noises from the dermoscopic images,such as hair,speckle,and impulse noise,and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network(U-Net).The framework uses variational Autoencoders(VAEs)for removing the hair noises,the Generative Adversarial Denoising Network(DGAN-Net),the Denoising U-shaped U-Net(D-U-NET),and Batch Renormalization U-Net(Br-U-NET)for remov-ing the speckle noise,and the Laplacian Vector Median Filter(MLVMF)for removing the impulse noise.In the second main stage,the residual attention u-net was used for segmentation.The framework achieves(35.11,31.26,27.01,and 26.16),(36.34,33.23,31.32,and 28.65),and(36.33,32.21,28.54,and 27.11)for removing hair,speckle,and impulse noise,respectively,based on Peak Signal Noise Ratio(PSNR)at the level of(0.1,0.25,0.5,and 0.75)of noise.The framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of noise.The experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure(SSIM)and PSNR and in the segmentation process as well.展开更多
In the literature,numerous techniques have been employed to decrease noise in medical image modalities,including X-Ray(XR),Ultrasonic(Us),Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and Positron Emission T...In the literature,numerous techniques have been employed to decrease noise in medical image modalities,including X-Ray(XR),Ultrasonic(Us),Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and Positron Emission Tomography(PET).These techniques are organized into two main classes:the Multiple Image(MI)and the Single Image(SI)techniques.In the MI techniques,images usually obtained for the same area scanned from different points of view are used.A single image is used in the entire procedure in the SI techniques.SI denoising techniques can be carried out both in a transform or spatial domain.This paper is concerned with single-image noise reduction techniques because we deal with single medical images.The most well-known spatial domain noise reduction techniques,including Gaussian filter,Kuan filter,Frost filter,Lee filter,Gabor filter,Median filter,Homomorphic filter,Speckle reducing anisotropic diffusion(SRAD),Nonlocal-Means(NL-Means),and Total Variation(TV),are studied.Also,the transform domain noise reduction techniques,including wavelet-based and Curvelet-based techniques,and some hybridization techniques are investigated.Finally,a deep(Convolutional Neural Network)CNN-based denoising model is proposed to eliminate Gaussian and Speckle noises in different medical image modalities.This model utilizes the Batch Normalization(BN)and the ReLU as a basic structure.As a result,it attained a considerable improvement over the traditional techniques.The previously mentioned techniques are evaluated and compared by calculating qualitative visual inspection and quantitative parameters like Peak Signal-to-Noise Ratio(PSNR),Correlation Coefficient(Cr),and system complexity to determine the optimum denoising algorithm to be applied universally.Based on the quality metrics,it is demonstrated that the proposed deep CNN-based denoising model is efficient and has superior denoising performance over the traditionaldenoising techniques.展开更多
Medical image segmentation is a crucial process for computer-aided diagnosis and surgery.Medical image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis...Medical image segmentation is a crucial process for computer-aided diagnosis and surgery.Medical image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and examination.Rician and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process negatively.Therefore,image enhancement has a significant role in MRI segmentation.This paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free image.The proposed techniques consider the values of Peak Signal to Noise Ratio(PSNR)and the level of noise as inputs to the attention-U-Net model for segmentation of the tumor.The framework has been divided into three stages:removing speckle and Rician noise,the segmentation stage,and the feature extraction stage.The framework presents solutions for each problem at a different stage of the segmentation.In the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the Rician.Afterwards,the most significant Rician noise-free images are passed to the three different methods:Deep Residual Network(DeRNet),Dilated Convolution Auto-encoder Denoising Network(Di-Conv-AE-Net),andDenoising Generative Adversarial Network(DGAN-Net)for removing the speckle noise.VMDand Bm3D have achieved PSNR values for levels of noise(0,0.25,0.5,0.75)for reducing the Rician noise by(35.243,32.135,28.214,24.124)and(36.11,31.212,26.215,24.123)respectively.The framework also achieved PSNR values for removing the speckle noise process for each level as follows:(34.146,30.313,28.125,24.001),(33.112,29.103,27.110,24.194),and(32.113,28.017,26.193,23.121)forDeRNet,Di-Conv-AE-Net,and DGAN-Net,respectively.The experiments that have been conducted have proved the efficiency of the proposed framework against classical filters such as Bilateral,Frost,Kuan,and Lee according to different levels of noise.The attention gate U-Net achieved 94.66 and 95.03 in the segmentation of free noise images in dice and accuracy,respectively.展开更多
SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and interpretation.Existing convolutional neural network(CNN)based despeckling methods have shown great performance in remo...SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and interpretation.Existing convolutional neural network(CNN)based despeckling methods have shown great performance in removing speckle noise.However,these CNN-basedmethods have a fewlimitations.They do not decouple complex background information in amulti-resolutionmanner.Moreover,they have deep network structures thatmay result in many parameters,limiting their applicability tomobile devices.Furthermore,extracting key speckle information in the presence of complex background is also a major problem with SAR.The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based despeckling(PAN-Despeck)network.The primary objective is to enhance image quality and enable improved information interpretation,particularly on mobile devices and scenarios involving complex backgrounds.The PAN-Despeck network leverages domainspecific knowledge and integrates Gaussian Laplacian image pyramid decomposition for multi-resolution image analysis.By utilizing this approach,complex background information can be effectively decoupled,leading to enhanced despeckling performance.Furthermore,the attention mechanism selectively focuses on key speckle features and facilitates complex background removal.The network incorporates recursive and residual blocks to ensure computational efficiency and accelerate training speed,making it lightweight while maintaining high performance.Through comprehensive evaluations,it is demonstrated that PAN-Despeck outperforms existing image restoration methods.With an impressive average peak signal-to-noise ratio(PSNR)of 28.355114 and a remarkable structural similarity index(SSIM)of 0.905467,it demonstrates exceptional performance in effectively reducing speckle noise in SAR images.The source code for the PAN-DeSpeck network is available on GitHub.展开更多
In this research, the denoising of speckled SAR image has been done with fuzzy filters (ATMED, TMED, ATMAV & TMAV). SAR image or Synthetic Aperture Radar image consists of the informatics of ISW (Internal solitary...In this research, the denoising of speckled SAR image has been done with fuzzy filters (ATMED, TMED, ATMAV & TMAV). SAR image or Synthetic Aperture Radar image consists of the informatics of ISW (Internal solitary waves). A new technique has been proposed which preserved the edge pixels by fuzzy edge detection method and then altered with the filtered image-pixels by fuzzy filtration for getting the denoised image. The comparative result shows that the proposed filter performs better than the other filtered results in terms of PSNR (41.61 dB), MAE (1.47), MSE (4.54) for TMAVxAPE & SSIM (81%) for ATMEDwAPE. The proposed method in this research shows better SSI (Spackle Suppression Index) value. Therefore the experimental result illustrates that the suggested fuzzy filter is much more capable of simultaneously protecting edges and suppressing speckle noise. This research will be beneficial to remove spackle noise from SAR images and can be used for remote sensing and mapping of surface area of earth.展开更多
Fresnel incoherent correlation holography(FINCH)is a well-established incoherent imaging technique.In FINCH,three selfinterference holograms are recorded with calculated phase differences between the two interfering,d...Fresnel incoherent correlation holography(FINCH)is a well-established incoherent imaging technique.In FINCH,three selfinterference holograms are recorded with calculated phase differences between the two interfering,differently modulated object waves and projected into a complex hologram.The object is reconstructed without the twin image and bias terms by a numerical Fresnel back propagation of the complex hologram.A modified approach to implement FINCH by a single camera shot by pre-calibrating the system involving recording of the point spread function library and reconstruction by a nonlinear cross correlation has been introduced recently.The expression of the imaging characteristics from the modulation functions in original FINCH and the modified approach by pre-calibration in spatial and polarization multiplexing schemes are reviewed.The study reveals that a reconstructing function completely independent of the function of the phase mask is required for the faithful expression of the characteristics of the modulating function in image reconstruction.In the polarization multiplexing method by non-linear cross correlation,a partial expression was observed,while in the spatial multiplexing method by non-linear cross correlation,the imaging characteristics converged towards a uniform behavior.展开更多
文摘The internal energy distribution of waves can be described using ocean-wave spectra.In many ways,obtaining wave spectra on a global scale is critical.Surface waves investigation and monitoring onboard the Chinese-French oceanography satellite is the first space-borne instrument for detecting wave spectra specially,which was launched on October 29,2018.It can avoid the shortage of synthetic aperture radar detection results while still having some problems,especially with the effects of speckle noise.In this study,a method to suppress the speckle noise is proposed.First,the empirical formula for background speckle noise is established.Second,many spatio-temporal representative fluctuation spectra are classified and averaged.Third,rational transfer function filtering is used to obtain speckle noise close to the along-track direction.Finally,a signal-to-noise ratio threshold is used to suppress the abnormal speckle noise.This method solves the problems existing in previous denoising methods,such as excessive denoising in the along-track direction and the inability of some abnormal noises to be denoised in the two-dimensional directional wave spectra.
文摘In spite of the advancement in computerized imaging,many image modalities produce images with commotion influencing both the visual quality and upsetting quantitative image analysis.In this way,the research in the zone of image denoising is very dynamic.Among an extraordinary assortment of image restoration and denoising techniques the neural network system-based noise sup-pression is a basic and productive methodology.In this paper,Bilateral Filter(BF)based Modular Neural Networks(MNN)has been utilized for speckle noise sup-pression in the ultrasound image.Initial step the BFfilter is used tofilter the input image.From the output of BF,statistical features such as mean,standard devia-tion,median and kurtosis have been extracted and these features are used to train the MNN.Then,thefiltered images from the BF are again denoised using MNN.The ultrasound dataset from the Kaggle site is used for the training and testing process.The simulation outcomes demonstrate that the BF-MNNfiltering method performs better for the multiplicative noise concealment in UltraSound(US)images.From the simulation results,it has been observed that BF-MNN performs better than the existing techniques in terms of peak signal to noise ratio(34.89),Structural Similarity Index(0.89)and Edge Preservation Index(0.67).
基金supported by the National Natural Science Foundation of China(No.61177018)the Program for New Century Excellent Talents in University(No.NECT-11-0596)+1 种基金the Key Program of Beijing Sci-ence and Technology Plan(No.D121100004812001)Beijing Nova Program(No.2011066)
文摘An integrated method based on optical and digital image processing is presented to suppress speckle in digital holography. A spatial light modulator is adopted to introduce random phases to the illuminating beam. Multiple holograms are reconstructed and superimposed, and the intensity is averaged to smooth the noise. The adaptive algorithm based on the nonlocal means is designed to further suppress the speckle. The presented method is compared with other methods reduction is improved, and the proposed method is effective The experimental results show that speckle and feasible.
基金supported by NSF of Jiangsu Province(No.BK20181483),NSFC(Nos.11671002,11701079,61731009)the Fundamental Research Funds for the Central Universities,and Science and Technology Commission of Shanghai Municipality(Nos.19JC1420102,18dz2271000)Hai Yan project,Lianyungang 521 project and NSF of HHIT(No.Z2017004).
文摘Ultrasound is a low-cost,non-invasive and real-time imaging modality that has proved popular for many medical applications.Unfortunately,the acquired ultrasound images are often corrupted by speckle noise from scatterers smaller than ultrasound beam wavelength.The signal-dependent speckle noise makes visual observation difficult.In this paper,we propose a patch-based low-rank approach for reducing the speckle noise in ultrasound images.After constructing the patch group of the ultrasound images by the block-matching scheme,we establish a variational model using the weighted nuclear norm as a regularizer for the patch group.The alternating direction method of multipliers(ADMM)is applied for solving the established nonconvex model.We return all the approximate patches to their original locations and get the final restored ultrasound images.Experimental results are given to demonstrate that the proposed method outperforms some existing state-of-the-art methods in terms of visual quality and quantitative measures.
文摘This article introduces a fastmeshless algorithm for the numerical solution nonlinear partial differential equations(PDE)by Radial Basis Functions(RBFs)approximation connected with the Total Variation(TV)-basedminimization functional and to show its application to image denoising containing multiplicative noise.These capabilities used within the proposed algorithm have not only the quality of image denoising,edge preservation but also the property of minimization of staircase effect which results in blocky effects in the images.It is worth mentioning that the recommended method can be easily employed for nonlinear problems due to the lack of dependence on a mesh or integration procedure.The numerical investigations and corresponding examples prove the effectiveness of the recommended algorithm regarding the robustness and visual improvement as well as peak-signal-to-noise ratio(PSNR),signal-to-noise ratio(SNR),and structural similarity index(SSIM)corresponded to the current conventional TV-based schemes.
基金This work was supported by the National Natural Science Foundation of China under Grant No. 60472048.
文摘Synthetic aperture radar （SAR） images are corrupted by multiplicative speckle noise which limits the performance of the classical coder/decoder algorithm in spatial domain. The relatively new transform of multiwavelets can possess desirable features simultaneously, such as orthogonality and symmetry, while scalar wavelets cannot. In this paper we propose a compression scheme combining with speckle noise reduction within the multiwavelet framework. Compared with classical set partitioning in hierarchical trees （SPIHT） algorithm, our method achieves favorable peak signal to noise ratio （PSNR） and superior speckle noise reduction performances.
基金The National Natural Science Foundation of China under contract No.61601132
文摘This paper proposes a speckle-suppression method for ocean internal solitary wave（ISW） synthetic aperture radar（SAR） images by using the curvelet transform.The band-shaped signatures of ocean ISWs in SAR images show obvious scale and directional characteristics.The curvelet transform possesses a very high scale and directional sensitivity.Therefore,the curvelet transform is very efficient in analyzing wave signals in SAR images.A noisy ocean ISW SAR image can be decomposed at different scales,directions,and positions using the curvelet transform.The information of the ISWs is centralized in the curvelet coefficients of certain directions under certain scales,whereas the speckle noise is distributed in every scale and direction.By manipulating the curvelet coefficients,the signals of the ISWs can be extracted from the noisy SAR image.Finally,the speckle noise is suppressed and the ISW feature is enhanced by adding the signals of the ISWs back to the original SAR image.Experiments demonstrate the effectiveness of this method.
文摘Computer vision is one of the significant trends in computer science.It plays as a vital role in many applications,especially in the medical field.Early detection and segmentation of different tumors is a big challenge in the medical world.The proposed framework uses ultrasound images from Kaggle,applying five diverse models to denoise the images,using the best possible noise-free image as input to the U-Net model for segmentation of the tumor,and then using the Convolution Neural Network(CNN)model to classify whether the tumor is benign,malignant,or normal.The main challenge faced by the framework in the segmentation is the speckle noise.It’s is a multiplicative and negative issue in breast ultrasound imaging,because of this noise,the image resolution and contrast become reduced,which affects the diagnostic value of this imaging modality.As result,speckle noise reduction is very vital for the segmentation process.The framework uses five models such as Generative Adversarial Denoising Network(DGAN-Net),Denoising U-Shaped Net(D-U-NET),Batch Renormalization U-Net(Br-UNET),Generative Adversarial Network(GAN),and Nonlocal Neutrosophic ofWiener Filtering(NLNWF)for reducing the speckle noise from the breast ultrasound images then choose the best image according to peak signal to noise ratio(PSNR)for each level of speckle-noise.The five used methods have been compared with classical filters such as Bilateral,Frost,Kuan,and Lee and they proved their efficiency according to PSNR in different levels of noise.The five diverse models are achieved PSNR results for speckle noise at level(0.1,0.25,0.5,0.75),(33.354,29.415,27.218,24.115),(31.424,28.353,27.246,24.244),(32.243,28.42,27.744,24.893),(31.234,28.212,26.983,23.234)and(33.013,29.491,28.556,25.011)forDGAN,Br-U-NET,D-U-NET,GANand NLNWF respectively.According to the value of PSNR and level of speckle noise,the best image passed for segmentation using U-Net and classification usingCNNto detect tumor type.The experiments proved the quality ofU-Net and CNN in segmentation and classification respectively,since they achieved 95.11 and 95.13 in segmentation and 95.55 and 95.67 in classification as dice score and accuracy respectively.
文摘Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion detection.This paper presents a novel framework for segmenting the skin.The framework contains two main stages:The first stage is for removing different types of noises from the dermoscopic images,such as hair,speckle,and impulse noise,and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network(U-Net).The framework uses variational Autoencoders(VAEs)for removing the hair noises,the Generative Adversarial Denoising Network(DGAN-Net),the Denoising U-shaped U-Net(D-U-NET),and Batch Renormalization U-Net(Br-U-NET)for remov-ing the speckle noise,and the Laplacian Vector Median Filter(MLVMF)for removing the impulse noise.In the second main stage,the residual attention u-net was used for segmentation.The framework achieves(35.11,31.26,27.01,and 26.16),(36.34,33.23,31.32,and 28.65),and(36.33,32.21,28.54,and 27.11)for removing hair,speckle,and impulse noise,respectively,based on Peak Signal Noise Ratio(PSNR)at the level of(0.1,0.25,0.5,and 0.75)of noise.The framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of noise.The experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure(SSIM)and PSNR and in the segmentation process as well.
文摘In the literature,numerous techniques have been employed to decrease noise in medical image modalities,including X-Ray(XR),Ultrasonic(Us),Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and Positron Emission Tomography(PET).These techniques are organized into two main classes:the Multiple Image(MI)and the Single Image(SI)techniques.In the MI techniques,images usually obtained for the same area scanned from different points of view are used.A single image is used in the entire procedure in the SI techniques.SI denoising techniques can be carried out both in a transform or spatial domain.This paper is concerned with single-image noise reduction techniques because we deal with single medical images.The most well-known spatial domain noise reduction techniques,including Gaussian filter,Kuan filter,Frost filter,Lee filter,Gabor filter,Median filter,Homomorphic filter,Speckle reducing anisotropic diffusion(SRAD),Nonlocal-Means(NL-Means),and Total Variation(TV),are studied.Also,the transform domain noise reduction techniques,including wavelet-based and Curvelet-based techniques,and some hybridization techniques are investigated.Finally,a deep(Convolutional Neural Network)CNN-based denoising model is proposed to eliminate Gaussian and Speckle noises in different medical image modalities.This model utilizes the Batch Normalization(BN)and the ReLU as a basic structure.As a result,it attained a considerable improvement over the traditional techniques.The previously mentioned techniques are evaluated and compared by calculating qualitative visual inspection and quantitative parameters like Peak Signal-to-Noise Ratio(PSNR),Correlation Coefficient(Cr),and system complexity to determine the optimum denoising algorithm to be applied universally.Based on the quality metrics,it is demonstrated that the proposed deep CNN-based denoising model is efficient and has superior denoising performance over the traditionaldenoising techniques.
文摘Medical image segmentation is a crucial process for computer-aided diagnosis and surgery.Medical image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and examination.Rician and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process negatively.Therefore,image enhancement has a significant role in MRI segmentation.This paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free image.The proposed techniques consider the values of Peak Signal to Noise Ratio(PSNR)and the level of noise as inputs to the attention-U-Net model for segmentation of the tumor.The framework has been divided into three stages:removing speckle and Rician noise,the segmentation stage,and the feature extraction stage.The framework presents solutions for each problem at a different stage of the segmentation.In the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the Rician.Afterwards,the most significant Rician noise-free images are passed to the three different methods:Deep Residual Network(DeRNet),Dilated Convolution Auto-encoder Denoising Network(Di-Conv-AE-Net),andDenoising Generative Adversarial Network(DGAN-Net)for removing the speckle noise.VMDand Bm3D have achieved PSNR values for levels of noise(0,0.25,0.5,0.75)for reducing the Rician noise by(35.243,32.135,28.214,24.124)and(36.11,31.212,26.215,24.123)respectively.The framework also achieved PSNR values for removing the speckle noise process for each level as follows:(34.146,30.313,28.125,24.001),(33.112,29.103,27.110,24.194),and(32.113,28.017,26.193,23.121)forDeRNet,Di-Conv-AE-Net,and DGAN-Net,respectively.The experiments that have been conducted have proved the efficiency of the proposed framework against classical filters such as Bilateral,Frost,Kuan,and Lee according to different levels of noise.The attention gate U-Net achieved 94.66 and 95.03 in the segmentation of free noise images in dice and accuracy,respectively.
文摘SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and interpretation.Existing convolutional neural network(CNN)based despeckling methods have shown great performance in removing speckle noise.However,these CNN-basedmethods have a fewlimitations.They do not decouple complex background information in amulti-resolutionmanner.Moreover,they have deep network structures thatmay result in many parameters,limiting their applicability tomobile devices.Furthermore,extracting key speckle information in the presence of complex background is also a major problem with SAR.The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based despeckling(PAN-Despeck)network.The primary objective is to enhance image quality and enable improved information interpretation,particularly on mobile devices and scenarios involving complex backgrounds.The PAN-Despeck network leverages domainspecific knowledge and integrates Gaussian Laplacian image pyramid decomposition for multi-resolution image analysis.By utilizing this approach,complex background information can be effectively decoupled,leading to enhanced despeckling performance.Furthermore,the attention mechanism selectively focuses on key speckle features and facilitates complex background removal.The network incorporates recursive and residual blocks to ensure computational efficiency and accelerate training speed,making it lightweight while maintaining high performance.Through comprehensive evaluations,it is demonstrated that PAN-Despeck outperforms existing image restoration methods.With an impressive average peak signal-to-noise ratio(PSNR)of 28.355114 and a remarkable structural similarity index(SSIM)of 0.905467,it demonstrates exceptional performance in effectively reducing speckle noise in SAR images.The source code for the PAN-DeSpeck network is available on GitHub.
文摘In this research, the denoising of speckled SAR image has been done with fuzzy filters (ATMED, TMED, ATMAV & TMAV). SAR image or Synthetic Aperture Radar image consists of the informatics of ISW (Internal solitary waves). A new technique has been proposed which preserved the edge pixels by fuzzy edge detection method and then altered with the filtered image-pixels by fuzzy filtration for getting the denoised image. The comparative result shows that the proposed filter performs better than the other filtered results in terms of PSNR (41.61 dB), MAE (1.47), MSE (4.54) for TMAVxAPE & SSIM (81%) for ATMEDwAPE. The proposed method in this research shows better SSI (Spackle Suppression Index) value. Therefore the experimental result illustrates that the suggested fuzzy filter is much more capable of simultaneously protecting edges and suppressing speckle noise. This research will be beneficial to remove spackle noise from SAR images and can be used for remote sensing and mapping of surface area of earth.
基金NATO grant No.SPS-985048Linkage grant No.LP190100505the Australian Research Council Discovery grant No.DP190103284。
文摘Fresnel incoherent correlation holography(FINCH)is a well-established incoherent imaging technique.In FINCH,three selfinterference holograms are recorded with calculated phase differences between the two interfering,differently modulated object waves and projected into a complex hologram.The object is reconstructed without the twin image and bias terms by a numerical Fresnel back propagation of the complex hologram.A modified approach to implement FINCH by a single camera shot by pre-calibrating the system involving recording of the point spread function library and reconstruction by a nonlinear cross correlation has been introduced recently.The expression of the imaging characteristics from the modulation functions in original FINCH and the modified approach by pre-calibration in spatial and polarization multiplexing schemes are reviewed.The study reveals that a reconstructing function completely independent of the function of the phase mask is required for the faithful expression of the characteristics of the modulating function in image reconstruction.In the polarization multiplexing method by non-linear cross correlation,a partial expression was observed,while in the spatial multiplexing method by non-linear cross correlation,the imaging characteristics converged towards a uniform behavior.
