Noise detection and removal for highly degraded images

Noise refers to any undesired signal which contaminates an image. Removal of noises from images is a critical issue in digital image processing. Image denoising is a very important processing task as a process itself or as a component of other processes. In the image processing period, the origin...

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Main Author: Guo, Huan.
Other Authors: Zhu Ce
Format: Final Year Project
Language:English
Published: 2011
Subjects:
Online Access:http://hdl.handle.net/10356/42819
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-428192023-07-07T15:48:12Z Noise detection and removal for highly degraded images Guo, Huan. Zhu Ce School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Noise refers to any undesired signal which contaminates an image. Removal of noises from images is a critical issue in digital image processing. Image denoising is a very important processing task as a process itself or as a component of other processes. In the image processing period, the original image may become degraded in various ways. Degradation may be in the form of random noise or interference, caused by camera mis-focus and motion, lens and film nonlinearity. The most important requirement of a good image denoising model is that it can remove noise while preserving edges. This report will present a good noise detection and removal toolbox with three different filtering approaches. The toolbox developed here is able to allow user to have a good understanding of noise reduction or removal in images using linear or non-linear filtering techniques applied to different kinds of noise. User can add noise through three types: Salt and Pepper, Gaussian and Speckle. Image restoration methods usually model the degradation process and apply an approximately inverse process to the degraded image to recover the original image. To restore images corrupted by noise, various image filtering strategies have been proposed. The toolbox covered three filtering type: Median, Averaging and Adaptive Filtering. Bachelor of Engineering 2011-01-14T06:02:52Z 2011-01-14T06:02:52Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/42819 en Nanyang Technological University 49 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Guo, Huan.
Noise detection and removal for highly degraded images
description Noise refers to any undesired signal which contaminates an image. Removal of noises from images is a critical issue in digital image processing. Image denoising is a very important processing task as a process itself or as a component of other processes. In the image processing period, the original image may become degraded in various ways. Degradation may be in the form of random noise or interference, caused by camera mis-focus and motion, lens and film nonlinearity. The most important requirement of a good image denoising model is that it can remove noise while preserving edges. This report will present a good noise detection and removal toolbox with three different filtering approaches. The toolbox developed here is able to allow user to have a good understanding of noise reduction or removal in images using linear or non-linear filtering techniques applied to different kinds of noise. User can add noise through three types: Salt and Pepper, Gaussian and Speckle. Image restoration methods usually model the degradation process and apply an approximately inverse process to the degraded image to recover the original image. To restore images corrupted by noise, various image filtering strategies have been proposed. The toolbox covered three filtering type: Median, Averaging and Adaptive Filtering.
author2 Zhu Ce
author_facet Zhu Ce
Guo, Huan.
format Final Year Project
author Guo, Huan.
author_sort Guo, Huan.
title Noise detection and removal for highly degraded images
title_short Noise detection and removal for highly degraded images
title_full Noise detection and removal for highly degraded images
title_fullStr Noise detection and removal for highly degraded images
title_full_unstemmed Noise detection and removal for highly degraded images
title_sort noise detection and removal for highly degraded images
publishDate 2011
url http://hdl.handle.net/10356/42819
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