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Chinese Journal of Lasers 2000年第2期9卷 182-186页

作者： JIANG Lihui WANG Chunhui WANG Qi SHANG Tieliang (National Key Laboratory of Tunable Laser Technology, Harbin Institute of Technology, Harbin 150001, China ) 哈尔滨工业大学可调谐(气体)激光技术国防科技重点实验室黑龙江哈尔滨

In this paper, a generalized parallel weighted-average multidirectional morphological filtering algorithm is presented to suppresing speckle noise. Simulation shows the algorithm can not only effectively suppress speckle noise but also preserve the geometrical features of images. Through comparison, it is proved that the algorithm is superior to F. Safa algoritm.

关键词： speckle noise morphological filter weighted averages parallel processing

speckle noise Suppression in Ultrasound Images Using Modular Neural Networks

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Intelligent Automation & Soft Computing 2023年第2期35卷 1753-1765页

作者： G.Karthiha Dr.S.Allwin Department of CSE Infant Jesus College of EngineeringAnna UniversityTuticorin628251India

In spite of the advancement in computerized imaging,many image modalities produce images with commotion inﬂuencing both the visual quality and upsetting quantitative image *** this way,the research in the zone of image denoising is very *** an extraordinary assortment of image restoration and denoising techniques the neural network system-based noise sup-pression is a basic and productive *** this paper,Bilateral Filter(BF)based Modular Neural Networks(MNN)has been utilized for speckle noise sup-pression in the ultrasound *** step the BFﬁlter is used toﬁlter the input *** 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 ***,theﬁltered images from the BF are again denoised using *** ultrasound dataset from the Kaggle site is used for the training and testing *** simulation outcomes demonstrate that the BF-MNNﬁltering method performs better for the multiplicative noise concealment in UltraSound(US)*** 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).

关键词： speckle noise bilateralﬁlter ultra-sound image MNN kurtosis

A speckle noise suppression method based on surface waves investigation and monitoring data

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Acta Oceanologica Sinica 2023年第1期42卷 131-141页

作者： Jingwei Gu Xiuzhong Li Yijun He School of Marine Sciences Nanjing University of Information Science&TechnologyNanjing210044China

The internal energy distribution of waves can be described using ocean-wave *** many ways,obtaining wave spectra on a global scale is *** 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,*** can avoid the shortage of synthetic aperture radar detection results while still having some problems,especially with the effects of speckle *** this study,a method to suppress the speckle noise is ***,the empirical formula for background speckle noise is ***,many spatio-temporal representative fluctuation spectra are classified and ***,rational transfer function filtering is used to obtain speckle noise close to the along-track ***,a signal-to-noise ratio threshold is used to suppress the abnormal speckle *** 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.

关键词： speckle noise surface waves investigation and monitoring WaveWatch III wave spectra

A Patch-Based Low-Rank Minimization Approach for speckle noise Reduction in Ultrasound Images

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Advances in Applied Mathematics and Mechanics 2022年第1期14卷 155-180页

作者： Xiao-Guang Lv Fang Li Jun Liu Sheng-Tai Lu School of Science Jiangsu Ocean UniversityLianyungangJiangsu 222005China Shanghai Key Laboratory of PMMP and School of Mathematical Sciences East China Normal UniversityShanghai 200241China School of Mathematics and Statistics and Key Laboratory of Applied Statistics of MOE Northeast Normal UniversityChangchunJilin 130024China

Ultrasound is a low-cost,non-invasive and real-time imaging modality that has proved popular for many medical ***,the acquired ultrasound images are often corrupted by speckle noise from scatterers smaller than ultrasound beam *** signal-dependent speckle noise makes visual observation *** this paper,we propose a patch-based low-rank approach for reducing the speckle noise in ultrasound *** 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 *** alternating direction method of multipliers(ADMM)is applied for solving the established nonconvex *** return all the approximate patches to their original locations and get the final restored ultrasound *** 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.

关键词： Ultrasound images patch speckle noise low-rank weighted nuclear norm minimization

Novel Framework of Segmentation 3D MRI of Brain Tumors

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Computers, Materials & Continua 2023年第2期74卷 3489-3502页

作者： Ibrahim Mahmoud El-Henawy Mostafa Elbaz Zainab H.Ali Noha Sakr Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Embedded Network Sys.and Tec. Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Medical image segmentation is a crucial process for computer-aided diagnosis and *** image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and *** and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process ***,image enhancement has a significant role in MRI *** 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 *** 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 *** framework has been divided into three stages:removing speckle and Rician noise,the segmentation stage,and the feature extraction *** framework presents solutions for each problem at a different stage of the *** the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the ***,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 *** 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)*** 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,*** experiments that have been conducted have proved the efficiency of the proposed framework a

关键词： MRI Rician noise speckle noise segmentation deep learning

speckle suppression in synthetic aperture radar ocean internal solitary wave images with curvelet transform

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Acta Oceanologica Sinica 2016年第9期35卷 13-21页

作者： ZHA Guozhena HE Qingyou GUAN Changlonga SUN Jian HE Mingxia College of Oceanic and Atmospheric Sciences Ocean University of ChinaQingdao 266100China State Key Laboratory of Tropical Oceanography South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China University of Chinese Academy of Sciences Beijing 100049China Physical Oceanography Laboratory Ocean University of ChinaQingdao 266100China Ocean Remote Sensing Institute Ocean University of ChinaQingdao 266100China

This paper proposes a speckle-suppression method for ocean internal solitary wave（ISW） synthetic aperture radar（SAR） images by using the curvelet *** band-shaped signatures of ocean ISWs in SAR images show obvious scale and directional *** curvelet transform possesses a very high scale and directional ***,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 *** 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 *** manipulating the curvelet coefficients,the signals of the ISWs can be extracted from the noisy SAR ***,the speckle noise is suppressed and the ISW feature is enhanced by adding the signals of the ISWs back to the original SAR *** demonstrate the effectiveness of this method.

关键词： curvelet transform internal solitary wave remote sensing speckle noise synthetic aperture radar

Deep CNN Model for Multimodal Medical Image Denoising

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Computers, Materials & Continua 2022年第11期73卷 3795-3814页

作者： Walid El-Shafai Amira A.Mahmoud Anas M.Ali El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

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)*** 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 *** denoising techniques can be carried out both in a transform or spatial *** paper is concerned with single-image noise reduction techniques because we deal with single medical *** 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 ***,the transform domain noise reduction techniques,including wavelet-based and Curvelet-based techniques,and some hybridization techniques are ***,a deep(Convolutional Neural Network)CNN-based denoising model is proposed to eliminate Gaussian and speckle noises in different medical image *** model utilizes the Batch Normalization(BN)and the ReLU as a basic *** a result,it attained a considerable improvement over the traditional *** 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 *** 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.

关键词： Image enhancement medical imaging speckle noise Gaussian noise denoising filters CNN denoising

A New Hybrid Model for Segmentation of the Skin Lesion Based on Residual Attention U-Net

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Computers, Materials & Continua 2023年第6期75卷 5177-5192页

作者： Saleh Naif Almuayqil Reham Arnous Noha Sakr Magdy M.Fadel Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and Technology(DHIET)MansouraEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion *** paper presents a novel framework for segmenting 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 *** the second main stage,the residual attention u-net was used for *** 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 *** 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 *** 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.

关键词： Skin tumor speckle noise impulse noise hair noise deep learning segmentation

PAN-DeSpeck:A Lightweight Pyramid and Attention-Based Network for SAR Image Despeckling

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Computers, Materials & Continua 2023年第9期76卷 3671-3689页

作者： Saima Yasmeen Muhammad Usman Yaseen Syed Sohaib Ali Moustafa M.Nasralla Sohaib Bin Altaf Khattak Department of Computer Sciences Comsats UniversityIslamabadPakistan Department of Research and Development Shearwater GeoservicesCrawleyWest SussexUK Department of Communications&Networks Engineering Prince Sultan UniversityRiyadhSaudi Arabia

SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and *** convolutional neural network(CNN)based despeckling methods have shown great performance in removing speckle ***,these CNN-basedmethods have a *** do not decouple complex background information in ***,they have deep network structures thatmay result in many parameters,limiting their applicability tomobile ***,extracting key speckle information in the presence of complex background is also a major problem with *** proposed study addresses these limitations by introducing a lightweight pyramid and attention-based despeckling(PAN-Despeck)*** primary objective is to enhance image quality and enable improved information interpretation,particularly on mobile devices and scenarios involving complex *** PAN-Despeck network leverages domainspecific knowledge and integrates Gaussian Laplacian image pyramid decomposition for multi-resolution image *** utilizing this approach,complex background information can be effectively decoupled,leading to enhanced despeckling ***,the attention mechanism selectively focuses on key speckle features and facilitates complex background *** network incorporates recursive and residual blocks to ensure computational efficiency and accelerate training speed,making it lightweight while maintaining high *** comprehensive evaluations,it is demonstrated that PAN-Despeck outperforms existing image restoration *** 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 *** source code for the PAN-DeSpeck network is available on GitHub.

关键词： Synthetic Aperture Radar(SAR) SAR image despeckling speckle noise deep learning pyramid networks multiscale image despeckling

Proposed Framework for Detection of Breast Tumors

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Computers, Materials & Continua 2023年第2期74卷 2927-2944页

作者： Mostafa Elbaz Haitham Elwahsh Ibrahim Mahmoud El-Henawy Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt

Computer vision is one of the significant trends in computer *** plays as a vital role in many applications,especially in the medical *** detection and segmentation of different tumors is a big challenge in the medical *** 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 *** main challenge faced by the framework in the segmentation is the speckle ***’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 *** result,speckle noise reduction is very vital for the segmentation *** 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 *** 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 *** 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 *** to the value of PSNR and level of speckle noise,the best image passed for segmentation using U-Net and classification usingCNNto detect tumor *** experiments proved

关键词： Breast tumor speckle noise GAN model U-Net model neutrosophic