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

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 denoi...

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Format:	Thesis
Language:	English
Subjects:	Digital images Noise Noise control Digital images > Editing > Computer programs Signal proccessing Denoising
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77165/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77165/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77165/4/Yahya%20Naji%20Saleh.pdf
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record_format	uketd_dc
spelling	my-unimap-771652022-11-24T06:33:38Z LMMSE-based low complexity image denoising in non-uniform directional filter bank Ruzelita, Ngadiran, Dr. 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. Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77165 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77165/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77165/1/Page%201-24.pdf cbe1118b4734fab494bc2425fc10c770 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77165/2/Full%20text.pdf e57e518eed5b8de3c0280875e6103d46 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77165/4/Yahya%20Naji%20Saleh.pdf b2d165b28c473651b9fe0ae77688abf0 Universiti Malaysia Perlis (UniMAP) Digital images Noise Noise control Digital images -- Editing -- Computer programs Signal proccessing Denoising School of Computer and Communication Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Ruzelita, Ngadiran, Dr.
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 LMMSE-based low complexity image denoising in non-uniform directional filter bank
description	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.
format	Thesis
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
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Computer and Communication Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77165/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77165/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77165/4/Yahya%20Naji%20Saleh.pdf
_version_	1776104233472360448

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

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