LMMSE-based low complexity image denoising in non-uniform directional filter bank

Master of Science in Computer Engineering

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Bibliographic Details
Main Author:	Yahya Naji Saleh, Obad
Other Authors:	Ruzelita, Ngadiran, Dr.
Format:	Thesis
Language:	English
Published:	Universiti Malaysia Perlis (UniMAP) 2016
Subjects:	Digital images Noise Noise control Digital images > Editing > Computer programs Signal proccessing Denoising
Online Access:	http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77165
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Institution:	Universiti Malaysia Perlis
Language:	English

id	my.unimap-77165
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spelling	my.unimap-771652022-11-24T06:33:38Z LMMSE-based low complexity image denoising in non-uniform directional filter bank Yahya Naji Saleh, Obad Ruzelita, Ngadiran, Dr. Digital images Noise Noise control Digital images -- Editing -- Computer programs Signal proccessing Denoising Master of Science in Computer Engineering This dissertation is about creating a non-redundant, effective and low-complexity denoising method. Denoising an image involves removing noise from an image to keep the original elements of the image and remove unwanted additions. Transform-based denoising depends on the transform used in the denoising method. Recent works focus on improving the performance of the denoising, ignoring the complexity. This work studies the complexity of well-known transforms that capture texture in images sufficiently, known as Contourlet transform (CT) and non-uniform directional filter bank (NUDFB). Complex wavelet transform (CWT), CT and non-subsampled Contourlet transform are examples of the base of denoising methods in the majority of current work. All these transforms perform well but they are complex and involve redundancy. Noise applied to images in this work is Gaussian noise and all images used are greyscale; the Linear Minimum Mean Square Error (LMMSE) method was used for denoising, with NUDFB as the base transform. The method decomposes the image using NUDFB then recognizes the coefficients to the step of denoising. LMMSE compares the coefficients in different resolutions to predict and remove noise. For measuring work accuracy, Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) are used. The resulting images from the proposed denoising method show improvement. The PSNR of the proposed method is higher than thresholding using NUDFB, about 1db. When comparing the proposed method with wavelet transform thresholding and CT, it has higher values than in CT and WT, especially in a high noise ratio. In images that contain directional information, such as fingerprint images, the proposed method has the highest SSIM. The proposed denoising method creates a way of denoising images using fewer requirements because of the low complexity. LMMSE also perform better than thresholding method. 2016 2022-11-24T06:31:18Z 2022-11-24T06:31:18Z Thesis http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77165 en Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP) School of Computer and Communication Engineering
institution	Universiti Malaysia Perlis
building	UniMAP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Perlis
content_source	UniMAP Library Digital Repository
url_provider	http://dspace.unimap.edu.my/
language	English
topic	Digital images Noise Noise control Digital images -- Editing -- Computer programs Signal proccessing Denoising
spellingShingle	Digital images Noise Noise control Digital images -- Editing -- Computer programs Signal proccessing Denoising Yahya Naji Saleh, Obad LMMSE-based low complexity image denoising in non-uniform directional filter bank
description	Master of Science in Computer Engineering
author2	Ruzelita, Ngadiran, Dr.
author_facet	Ruzelita, Ngadiran, Dr. Yahya Naji Saleh, Obad
format	Thesis
author	Yahya Naji Saleh, Obad
author_sort	Yahya Naji Saleh, Obad
title	LMMSE-based low complexity image denoising in non-uniform directional filter bank
title_short	LMMSE-based low complexity image denoising in non-uniform directional filter bank
title_full	LMMSE-based low complexity image denoising in non-uniform directional filter bank
title_fullStr	LMMSE-based low complexity image denoising in non-uniform directional filter bank
title_full_unstemmed	LMMSE-based low complexity image denoising in non-uniform directional filter bank
title_sort	lmmse-based low complexity image denoising in non-uniform directional filter bank
publisher	Universiti Malaysia Perlis (UniMAP)
publishDate	2016
url	http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77165
_version_	1753972981961326592

LMMSE-based low complexity image denoising in non-uniform directional filter bank

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