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An adaptive denoising method for colour images of mobile phone based on bivariate shrinkage function

INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS 2018年第6期10卷 484-493页

作者： Wu, Xuehui Lu, Xiaobo Han, Xue Liu, Chunxue Southeast Univ Sch Automat Nanjing Jiangsu Peoples R China Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing 210096 Jiangsu Peoples R China

The photography function of mobile phones has become the current trend, and the requirements for image denoising performance are higher and higher. It is different from traditional image denoising because mobile phone image denoising needs faster speed, less computing time and higher efficiency. Wavelet transform denoising algorithm can meet the denoising demand of mobile phone images because of its rapidity, validity and so on. However, denoising by wavelet transform is optimal for Gaussian noise, and the actual noise of images taken by mobile phones does not completely conform to Gaussian distribution, and then down-sampling method is adopted to simulate the Gaussian noise. In this article, an adaptive denoising method for colour images of mobile phone based on bivariate shrinkage function is proposed. Simulation and actual experimental results showed that the method can get better denoising effect compared with other methods.

关键词： mobile phone image denoising down sampling bivariate shrinkage function noise variance greying

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Denoising of ECG signals using weighted stationary wavelet total variation

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期73卷 103478-103478页

作者： Madan, Parul Singh, Vijay Singh, Devesh Pratap Diwakar, Manoj Kishor, Avadh Graph Era Deemed Univ Comp Sci & Engn Dehra Dun Uttarakhand India Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala Punjab India

ECG signals capture the electrical activity of the heart and can be used to determine its health. The P-QRS-T morphological features of the ECG may be used to identify a variety of cardiac problems. The existence of various disturbances in ECG signals, on the other hand, has a significant impact on correct feature extraction. During acquisition, various noises affect ECG readings, including power line interference, baseline wandering, motion artifacts, and electromyogram noise. In this paper, we proposed an algorithm called STationary Wavelet Total Variation (STWaTV) that introduces a total variation (TV) method with a bivariate shrinkage rule in the stationary wavelet transform (SWT) domain. STWaTV is tested with three existing threshold schemes: firm threshold, hard threshold, and bivariate shrinkage function with SWT for thresholding coefficients and TV for minimizing an objective function. To evaluate the effectiveness of STWaTV, we consider the standard databases and perform the comparative study along with existing state-of-the-art methods. In terms of qualitative analysis of STWaTV, the most significant signal to noise ratio (SNR) is in the range 13.23 to 53.61, root mean square error (RMSE) in the range 0.0006 to 1.1000, and the percentage root mean square difference (PRD) in the range 0.095 to 1.200. In sum, STWaTV outperform its counterparts and denoise ECG data without altering the amplitude of the original signals.

关键词： Discrete wavelet transform (DWT) Stationary wavelet transform(SWT) Total variation (TV) bivariate shrinkage function Electromyographic (EMG)

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Vector extension of quaternion wavelet transform and its application to colour image denoising

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IET SIGNAL PROCESSING 2019年第2期13卷 133-140页

作者： Gai, Shan Bao, Zhongyun Zhang, Kaige Nanchang Hangkong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China Utah State Univ Dept Comp Sci Logan UT 84322 USA

In this study, the authors study and give a new framework for colour image representation based on colour quaternion wavelet transform (CQWT). The new colour quaternion filter bank is constructed by using radon transform. Starting from link with structure tensors, the authors propose a new multi-scale tool for vector-valued signals which can provide efficient analysis of local features by using the concepts of amplitude, phase, and orientation. To demonstrate the properties of CQWT, new colour image denoising algorithm is proposed by using CQWT and bivariate shrinkage function. The performance of the proposed algorithm is experimentally verified on a variety of noise levels. Experimental results show that the proposed algorithm achieves superior performance both in visual quality and objective peak-signal-to-noise ratio, mean square error, and structure similarity values, compared with other state-of-the-art denoising algorithms.

关键词： image representation image denoising filtering theory image colour analysis Radon transforms wavelet transforms efficient analysis local features CQWT colour image denoising algorithm bivariate shrinkage function experimental results objective peak-signal-to-noise ratio structure similarity values state-of-the-art denoising algorithms authors study colour image representation continuous quaternion wavelet colour quaternion filter bank structure tensors multiscale tool vector-valued signals

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Directional tensor product complex tight framelets for compressed sensing MRI reconstruction

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IET IMAGE PROCESSING 2019年第12期13卷 2183-2189页

作者： Jiang, Mingfeng Lu, Liang Shen, Yi Wu, Long Gong, Yinglan Xia, Ling Liu, Feng Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou Zhejiang Peoples R China Zhejiang Sci Tech Univ Dept Math Hangzhou Zhejiang Peoples R China Zhejiang Univ Dept Biomed Engn Hangzhou Zhejiang Peoples R China Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia

Compressed sensing magnetic resonance imaging (CS-MRI) is an effective way of reducing the sampling data in the k-space and shortening the scanning time. Motivated by the high performance of directional tensor product complex tight framelets (TPCTFs) for the image denoising problem, the authors proposed a novel framework that integrated TPCTF for sparse representation and projected fast iterative soft-thresholding algorithm (pFISTA) for CS-MRI reconstruction. Furthermore, to take advantage of the cross-scale relations in the wavelet tree of frame coefficients, the bivariate shrinkage (BS) function with local variance estimation is proposed to shrink thresholding. Such TPCTFs can provide sparse directional representations very well for MR image. When compared with other the state-of-the-art CS-MRI algorithms in numerical experiments, the proposed TPCTF-BS method achieves a higher reconstruction quality with respect to image edge preservation and the artefact suppression.

关键词： wavelet transforms tensors biomedical MRI image reconstruction medical image processing compressed sensing iterative methods image denoising image representation trees (mathematics) directional tensor product complex tight framelets compressed sensing MRI reconstruction image denoising problem soft-thresholding algorithm CS-MRI reconstruction sparse directional representations compressed magnetic resonance imaging projected fast iterative soft-thresholding algorithm cross-scale relations wavelet tree bivariate shrinkage function local variance estimation shrink thresholding image edge preservation artefact suppression

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ECG denoising on bivariate shrinkage function exploiting interscale dependency of wavelet coefficients

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2011年第3期19卷 495-511页

作者： Kayhan, Sema Ercelebi, Ergun Gaziantep Univ Dept Elect & Elect Engn TR-27310 Gaziantep Turkey

This paper presents a new method for electrocardiogram (EGG) denoising based on bivariate shrinkage functions exploiting the interscale dependency of wavelet coefficients. Most nonlinear thresholding methods based on wavelet transform denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of ECG signals have significant dependencies. In this paper, we proposed a new method by considering the dependencies between the coefficients and their parents in detail on a bivariate shrinkage function for denoising of an ECG signal corrupted by different types of noises, such as muscle artifact noise, electrode motion, and white noise. In real-time applications, reduction of computational time is very crucial, so we constructed the wavelet transform by lifting scheme. The lifting scheme is a new technique to construct wavelet transform;namely, second generation wavelet transform is an alternative and faster algorithm for a classical wavelet transform. The overall denoising performance of our proposed method was considered in relation to several measuring parameters, including types of wavelet filters (Daubechies 4 (DB4), Daubechies 6 (DB6), and Daubechies 8 (DB8)) and decomposition depth. Global performance was evaluated by means of the signal-to-noise ratio (SNR) and visual inspection. We used a set of MIT-BIH arrhythmia database ECG records. To evaluate our method, a comparative study was carried out that referred to effective data-driven techniques in the literature, namely VisuShrink, SureShrink, BayesShrink, and level-dependent threshold estimation. The experimental results indicated that the proposed methods in the paper were better than the compared methods in, terms of retaining the geometrical characteristics of the ECG signal, SNR. Due to its simplicity and its fast implementation, the method can easily be used in clinical medicine.

关键词： Lifting scheme bivariate shrinkage function ECG denoising

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Spatially adaptive image denoising using inter-scale dependence in directionlet domain

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IET IMAGE PROCESSING 2015年第2期9卷 142-152页

作者： Sethunadh, R. Thomas, Tessamma Indian Space Res Org ISRO Vikram Sarabhai Space Ctr Trivandrum Kerala India Cochin Univ Sci & Technol CUSAT Image & Video Proc Lab Cochin Kerala India

The performance of image processing algorithms can be significantly improved by the application of multi-resolution image representation with directional features. Directionlet transform (DT) is one such representation which has gained popularity over the past few years as an anisotropic, perfect reconstruction and critically sampled basis function with directional vanishing moments along any two directions. In this study, the authors propose a spatially adaptive image denoising scheme for Gaussian noise based on DT by considering the dependences of the directionlet coefficients across different scales. The image is first decomposed using DT and the coefficients so obtained are modelled using a bivariate heavy tailed pdf' with a local variance parameter to account for inter- and intra-scale dependencies of the coefficients. The DT is made adaptive to the local dominant directions in the image by identifying the dominant directions in the spatially segmented image through the computation of a parameter called directional variance'. Bayesian maximum a posteriori' estimator is then used to compute the noise free coefficients from the bivariate models of the signal and noise. The denoised image is obtained from the transform coefficients, which were modified using the bivariate shrinkage function, using directional information and inverse DT. Experimental results show that the bivariate shrinkage in directionlet domain achieves better performance than that in wavelet domain, in terms of numerical and perceptual quality.

关键词： image denoising image resolution image representation Gaussian noise image segmentation transforms spatially adaptive image denoising interscale dependence directionlet domain image processing algorithms multiresolution image representation directional features DT sampled basis function directional vanishing moments Gaussian noise directionlet coefficients image segmentation directional variance noise free coefficients bivariate models transform coefficients bivariate shrinkage function

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A Wavelet-Based Denoising Method for Color Image of Mobile Phone 11

A Wavelet-Based Denoising Method for Color Image of Mobile P...

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11th International Conference on Natural Computation (ICNC) / 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Wu, Xuehui Lu, Xiaobo Han, Xue Liu, Chunxue Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China Southeast Univ Minist Educ Key Lab Measurement & Control CSE Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781467376792

In modern society, people's requirement of mobile phone camera is higher and higher, its image denoising method is also becoming the focus of attention. In this paper, starting from the actual requirements of mobile phone image denoising, an adaptive mobile phone image denoising method based on bivariate shrinkage function was proposed. Firstly, the actual image noise was made similar to the Gaussian noise by down sampling process, and then a kind of adaptive noise variance of bivariate shrinkage function based on Bayesian denoising method was put forward to estimate variance of the down sampled image. Finally, the denoised image was grayed to further eliminate the noise in the flat areas. Simulation and actual experimental results showed that the method of this paper can get better denoising effect compared with the existing mainstream methods.

关键词： phone image wavelet-based denosing bivariate shrinkage function noise variance graying

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Image denoising algorithm based on contourlet transform for optical coherence tomography heart tube image

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IET IMAGE PROCESSING 2013年第5期7卷 442-450页

作者： Guo, Qing Dong, Fangmin Sun, Shuifa Lei, Bangjun Gao, Bruce Z. China Three Gorges Univ Inst Intelligent Vis & Image Informat Yichang 443002 Hubei Peoples R China Clemson Univ Dept Bioengn Clemson SC 29634 USA

Optical coherence tomography (OCT) is becoming an increasingly important imaging technology in the Biomedical field. However, the application of OCT is limited by the ubiquitous noise. In this study, the noise of OCT heart tube image is first verified as being multiplicative based on the local statistics (i.e. the linear relationship between the mean and the standard deviation of certain flat area). The variance of the noise is evaluated in log-domain. Based on these, a joint probability density function is constructed to take the inter-direction dependency in the contourlet domain from the logarithmic transformed image into account. Then, a bivariate shrinkage function is derived to denoise the image by the maximum a posteriori estimation. Systemic comparative experiments are made to synthesis images, OCT heart tube images and other OCT tissue images by subjective assessment and objective metrics. The experiment results are analysed based on the denoising results and the predominance degree of the proposed algorithm with respect to the wavelet-based algorithm. The results show that the proposed algorithm improves the signal-to-noise ratio, whereas preserving the edges and has more advantages on the images containing multi-direction information like OCT heart tube image.

关键词： cardiology image denoising maximum likelihood estimation medical image processing optical tomography probability wavelet transforms image denoising algorithm contourlet transform optical coherence tomography heart tube image OCT heart tube image imaging technology biomedical field local statistics noise variance joint probability density function logarithmic image transformation bivariate shrinkage function maximum a posteriori estimation OCT tissue images subjective assessment objective metrics wavelet-based algorithm signal-to-noise ratio improvement

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An Image De-noising Algorithm based on the Dual-Tree Complex Wavelet

An Image De-noising Algorithm based on the Dual-Tree Complex...

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7th International Conference on MEMS, NANO and Smart Systems (ICMENS 2011)

作者： Ning, Yongmin Li, Lingling Shi, Hua Chen, Jing Luoyang Res Inst Electroopt Equipment Luoyang 471009 Peoples R China Zhengzhou Inst Aeronaut Ind Management Dept Comp Sci & Applicat Zhengzhou 450015 Peoples R China Xiamen Univ Sch Informat Sci & Technol Xiamen 361005 Peoples R China

ISBN: (纸本)9783037853122

In this paper, an image de-noising algorithm based on the Dual-Tree Complex Wavelet Transform (DT-CWT) is proposed, which takes the advantage of redundant coefficients transformed by DT-CWT. The model of bivariate shrinkage function is used to provide a nonlinear threshold strategy. It exploits the dependency between inter-scale parents and children coefficients to recover the original coefficients more accurate. With the shift-invariance property of DT-CWT coefficients, the algorithm prevents the Gibbs effect caused by the thresholding, which further improves the reconstructed quality. Experiment results show that the de-noised image using DT-CWT can achieve more than ldB prior to DWT with impressive visual results.

关键词： Dual-Tree Complex Wavelet Transform image de-noise bivariate shrinkage function shift-invariance

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Bi-ComForWaRD: bivariate COMPLEX FOURIER-WAVELET REGULARIZED DECONVOLUTION FOR MEDICAL IMAGING

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2009年第2期8卷 85-95页

作者： Rao, M. Venu Gopala Vathsal, S. Narasaraopeta Engn Coll Guntur Dt AP India JBIET Hyderabad Andhra Pradesh India

In this paper, we propose a new hybrid bivariate Complex Fourier Wavelet Regularized Deconvolution (Bi-ComForWaRD) that is an extension to the ComForWaRD algorithm, for medical imaging. This new algorithm is a two-step process, a global blur compensation using generalized Wiener filter and followed by a denoising algorithm using local adaptive bivariate shrinkage function. It is a low-complexity denoising algorithm using the joint statistics of the wavelet coefficients and considers the statistical dependencies between the coefficients. And also, the performance of this system will be demonstrated on both the orthogonal wavelet transform and the dual-tree complex wavelet transform (DT-CWT) and some comparisons with the best available wavelet-based image denoising results will be given in order to illustrate the effectiveness of the system.

关键词： Computed tomography complex wavelets medical imaging bivariate shrinkage function deconvolution denoising

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