Image restoration using regularized inverse filtering and adaptive threshold wavelet denoising

Although the Wiener filtering is the optimal tradeoff of inverse filtering and noise smoothing, in the case when the blurring filter is singular, the Wiener filtering actually amplify the noise. This suggests that a denoising step is needed to remove the amplified noise .Wavelet-based denoising sch...

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Autor principal:	Firas Ali
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2007
Materias:	Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/553213a17c4d47488f23299cca7df094
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spelling	oai:doaj.org-article:553213a17c4d47488f23299cca7df0942021-12-02T04:15:16ZImage restoration using regularized inverse filtering and adaptive threshold wavelet denoising1818-11712312-0789https://doaj.org/article/553213a17c4d47488f23299cca7df0942007-03-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/620https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 Although the Wiener filtering is the optimal tradeoff of inverse filtering and noise smoothing, in the case when the blurring filter is singular, the Wiener filtering actually amplify the noise. This suggests that a denoising step is needed to remove the amplified noise .Wavelet-based denoising scheme provides a natural technique for this purpose . In this paper a new image restoration scheme is proposed, the scheme contains two separate steps : Fourier-domain inverse filtering and wavelet-domain image denoising. The first stage is Wiener filtering of the input image , the filtered image is inputted to adaptive threshold wavelet denoising stage . The choice of the threshold estimation is carried out by analyzing the statistical parameters of the wavelet sub band coefficients like standard deviation, arithmetic mean and geometrical mean . The noisy image is first decomposed into many levels to obtain different frequency bands. Then soft thresholding method is used to remove the noisy coefficients, by fixing the optimum thresholding value by this method . Experimental results on test image by using this method show that this method yields significantly superior image quality and better Peak Signal to Noise Ratio (PSNR). Here, to prove the efficiency of this method in image restoration , we have compared this with various restoration methods like Wiener filter alone and inverse filter. Firas AliAl-Khwarizmi College of Engineering – University of BaghdadarticleChemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 3, Iss 1 (2007)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Firas Ali Image restoration using regularized inverse filtering and adaptive threshold wavelet denoising
description	Although the Wiener filtering is the optimal tradeoff of inverse filtering and noise smoothing, in the case when the blurring filter is singular, the Wiener filtering actually amplify the noise. This suggests that a denoising step is needed to remove the amplified noise .Wavelet-based denoising scheme provides a natural technique for this purpose . In this paper a new image restoration scheme is proposed, the scheme contains two separate steps : Fourier-domain inverse filtering and wavelet-domain image denoising. The first stage is Wiener filtering of the input image , the filtered image is inputted to adaptive threshold wavelet denoising stage . The choice of the threshold estimation is carried out by analyzing the statistical parameters of the wavelet sub band coefficients like standard deviation, arithmetic mean and geometrical mean . The noisy image is first decomposed into many levels to obtain different frequency bands. Then soft thresholding method is used to remove the noisy coefficients, by fixing the optimum thresholding value by this method . Experimental results on test image by using this method show that this method yields significantly superior image quality and better Peak Signal to Noise Ratio (PSNR). Here, to prove the efficiency of this method in image restoration , we have compared this with various restoration methods like Wiener filter alone and inverse filter.
format	article
author	Firas Ali
author_facet	Firas Ali
author_sort	Firas Ali
title	Image restoration using regularized inverse filtering and adaptive threshold wavelet denoising
title_short	Image restoration using regularized inverse filtering and adaptive threshold wavelet denoising
title_full	Image restoration using regularized inverse filtering and adaptive threshold wavelet denoising
title_fullStr	Image restoration using regularized inverse filtering and adaptive threshold wavelet denoising
title_full_unstemmed	Image restoration using regularized inverse filtering and adaptive threshold wavelet denoising
title_sort	image restoration using regularized inverse filtering and adaptive threshold wavelet denoising
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2007
url	https://doaj.org/article/553213a17c4d47488f23299cca7df094
work_keys_str_mv	AT firasali imagerestorationusingregularizedinversefilteringandadaptivethresholdwaveletdenoising
_version_	1718401366361112576

Image restoration using regularized inverse filtering and adaptive threshold wavelet denoising

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