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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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/42819 |
| _version_ | 1824453552515317760 |
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| author | Guo, Huan. |
| author2 | Zhu Ce |
| author_facet | Zhu Ce Guo, Huan. |
| author_sort | Guo, Huan. |
| collection | NTU |
| 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. |
| first_indexed | 2025-02-19T03:08:14Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/42819 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:08:14Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Guo, Huan. Noise detection and removal for highly degraded images |
| title | 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_short | Noise detection and removal for highly degraded images |
| title_sort | noise detection and removal for highly degraded images |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/42819 |
| work_keys_str_mv | AT guohuan noisedetectionandremovalforhighlydegradedimages |