Image noise removal and detection
In this paper, the effect of different types of noises on images are shown as wells as the various method of detecting and removing noise. Here MATLAB is used as the image processing tool to detect and remove unwanted noises. Most images captured are affected by random noises by some way or the...
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sg-ntu-dr.10356-1537702023-07-07T17:56:41Z Image noise removal and detection Edirachcharige Sahan Ruchira Jinadasa Kai-Kuang Ma School of Electrical and Electronic Engineering EKKMA@ntu.edu.sg Engineering::Electrical and electronic engineering::Applications of electronics In this paper, the effect of different types of noises on images are shown as wells as the various method of detecting and removing noise. Here MATLAB is used as the image processing tool to detect and remove unwanted noises. Most images captured are affected by random noises by some way or the other. Although this noise might seem random to the untrained eye, what lies beneath is a pattern and a root cause. It is crucial for us to understand the different types of noises and their origins before diving into noise detection and de-noising. Therefore, these random noises are identified and explained. Further clarification is given as to how and why these noises are present in images. After grasping the concept of noise, the report will continue by demonstrating the different tools and filters used to overcome these noises. These de-noising methods will be demonstrated on grey-scale images as well as RGB images. Further research was done into the most prevalent noise which is Gaussian noise and how we can rectify images affected by it. After understanding the different types of noises one simple method of detecting noise in image is discussed. Histogram analysis is done on the image to identify the type of noise affecting the image. Next the research was conducted on noise found in digital photography. Two types of noises affecting digital photography is introduced in this report. The negative and positive repercussions of these noises are demonstrated by illustrations here. Here a powerful de-noising software called Light-room is discussed and compared with a much simpler program using MATLAB. These two entities were tested on de-noising corrupted digital images as well as its limitations when enhancing underexposed images. Lastly research was done on the niche area of underwater imagery. Several research and papers has been published regarding this field and in this chapter few of these methods is discussed in detail. Furthermore, a much simpler but productive method of enhancing underwater imagery using MATLAB is discussed. These two methods are compared regarding their effectiveness in enhancing the image. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-12-10T12:15:47Z 2021-12-10T12:15:47Z 2021 Final Year Project (FYP) Edirachcharige Sahan Ruchira Jinadasa (2021). Image noise removal and detection. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153770 https://hdl.handle.net/10356/153770 en P3029-201 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering::Applications of electronics Edirachcharige Sahan Ruchira Jinadasa Image noise removal and detection |
description |
In this paper, the effect of different types of noises on images are shown as wells as the various method of
detecting and removing noise. Here MATLAB is used as the image processing tool to detect and remove
unwanted noises. Most images captured are affected by random noises by some way or the other.
Although this noise might seem random to the untrained eye, what lies beneath is a pattern and a root
cause. It is crucial for us to understand the different types of noises and their origins before diving into
noise detection and de-noising. Therefore, these random noises are identified and explained. Further
clarification is given as to how and why these noises are present in images. After grasping the concept of
noise, the report will continue by demonstrating the different tools and filters used to overcome these
noises. These de-noising methods will be demonstrated on grey-scale images as well as RGB images.
Further research was done into the most prevalent noise which is Gaussian noise and how we can rectify
images affected by it. After understanding the different types of noises one simple method of detecting
noise in image is discussed. Histogram analysis is done on the image to identify the type of noise
affecting the image.
Next the research was conducted on noise found in digital photography. Two types of noises affecting
digital photography is introduced in this report. The negative and positive repercussions of these noises
are demonstrated by illustrations here. Here a powerful de-noising software called Light-room is discussed
and compared with a much simpler program using MATLAB. These two entities were tested on
de-noising corrupted digital images as well as its limitations when enhancing underexposed images.
Lastly research was done on the niche area of underwater imagery. Several research and papers has been published regarding this field and in this chapter few of these methods is discussed in detail. Furthermore, a much simpler but productive method of enhancing underwater imagery using MATLAB is discussed. These two methods are compared regarding their effectiveness in enhancing the image. |
author2 |
Kai-Kuang Ma |
author_facet |
Kai-Kuang Ma Edirachcharige Sahan Ruchira Jinadasa |
format |
Final Year Project |
author |
Edirachcharige Sahan Ruchira Jinadasa |
author_sort |
Edirachcharige Sahan Ruchira Jinadasa |
title |
Image noise removal and detection |
title_short |
Image noise removal and detection |
title_full |
Image noise removal and detection |
title_fullStr |
Image noise removal and detection |
title_full_unstemmed |
Image noise removal and detection |
title_sort |
image noise removal and detection |
publisher |
Nanyang Technological University |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/153770 |
_version_ |
1772828925233201152 |