Integration of enhanced dictionary learning and magnitude computation techniques for removing rain streaks in digital image enhancement

Rain streaks detection and removal are very important topics in the field of image processing and computer vision. The present of rain in images and videos causing the pixels in the image corrupted. The main problem in this research is to detect and remove rain streaks. The aim of this research is t...

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Main Author:	Raima Hassim
Format:	Thesis
Language:	English English
Published:	2017
Subjects:	TA1501-1820 Applied optics Photonics
Online Access:	https://eprints.ums.edu.my/id/eprint/38979/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/38979/2/FULLTEXT.pdf
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spelling	my-ums-ep.389792024-06-28T06:59:04Z Integration of enhanced dictionary learning and magnitude computation techniques for removing rain streaks in digital image enhancement 2017 Raima Hassim TA1501-1820 Applied optics. Photonics Rain streaks detection and removal are very important topics in the field of image processing and computer vision. The present of rain in images and videos causing the pixels in the image corrupted. The main problem in this research is to detect and remove rain streaks. The aim of this research is to develop an efficient technique that able to detect and remove rain streaks from a single image. The proposed technique consists of two main parts which are the rain streaks detection and rain streaks removal. Both rain streaks detection and rain streaks removal are combined and known as HyDRa. At first, the contrast of the image will be enhanced followed by bilateral filtering technique to divide the input image into two parts, low frequency, and high-frequency part. The classification of rain component and non-rain component is done when the high-frequency part of the image undergoes the dictionary learning approach. For achieving smooth detection of the rain streaks process, the magnitude of each pixel in the rain component will be computed. As for the removal stages, the non-rain component is subtracted from the image and will be combined with the low-frequency part from the filtering stage. The PSNR test and SSiM test of HyDRa for image 1 are 31.09 dB and 0.9194 respectively. Based on the performance test, the PSNR values for test images are significantly better as compared to the classical technique such bilateral filtering approach and self-learning dictionary approach. 2017 Thesis https://eprints.ums.edu.my/id/eprint/38979/ https://eprints.ums.edu.my/id/eprint/38979/1/24%20PAGES.pdf text en public https://eprints.ums.edu.my/id/eprint/38979/2/FULLTEXT.pdf text en validuser masters Universiti Malaysia Sabah Fakulti Sains dan Sumber Alam
institution	Universiti Malaysia Sabah
collection	UMS Institutional Repository
language	English English
topic	TA1501-1820 Applied optics Photonics
spellingShingle	TA1501-1820 Applied optics Photonics Raima Hassim Integration of enhanced dictionary learning and magnitude computation techniques for removing rain streaks in digital image enhancement
description	Rain streaks detection and removal are very important topics in the field of image processing and computer vision. The present of rain in images and videos causing the pixels in the image corrupted. The main problem in this research is to detect and remove rain streaks. The aim of this research is to develop an efficient technique that able to detect and remove rain streaks from a single image. The proposed technique consists of two main parts which are the rain streaks detection and rain streaks removal. Both rain streaks detection and rain streaks removal are combined and known as HyDRa. At first, the contrast of the image will be enhanced followed by bilateral filtering technique to divide the input image into two parts, low frequency, and high-frequency part. The classification of rain component and non-rain component is done when the high-frequency part of the image undergoes the dictionary learning approach. For achieving smooth detection of the rain streaks process, the magnitude of each pixel in the rain component will be computed. As for the removal stages, the non-rain component is subtracted from the image and will be combined with the low-frequency part from the filtering stage. The PSNR test and SSiM test of HyDRa for image 1 are 31.09 dB and 0.9194 respectively. Based on the performance test, the PSNR values for test images are significantly better as compared to the classical technique such bilateral filtering approach and self-learning dictionary approach.
format	Thesis
qualification_level	Master's degree
author	Raima Hassim
author_facet	Raima Hassim
author_sort	Raima Hassim
title	Integration of enhanced dictionary learning and magnitude computation techniques for removing rain streaks in digital image enhancement
title_short	Integration of enhanced dictionary learning and magnitude computation techniques for removing rain streaks in digital image enhancement
title_full	Integration of enhanced dictionary learning and magnitude computation techniques for removing rain streaks in digital image enhancement
title_fullStr	Integration of enhanced dictionary learning and magnitude computation techniques for removing rain streaks in digital image enhancement
title_full_unstemmed	Integration of enhanced dictionary learning and magnitude computation techniques for removing rain streaks in digital image enhancement
title_sort	integration of enhanced dictionary learning and magnitude computation techniques for removing rain streaks in digital image enhancement
granting_institution	Universiti Malaysia Sabah
granting_department	Fakulti Sains dan Sumber Alam
publishDate	2017
url	https://eprints.ums.edu.my/id/eprint/38979/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/38979/2/FULLTEXT.pdf
_version_	1804890358272753664

Integration of enhanced dictionary learning and magnitude computation techniques for removing rain streaks in digital image enhancement

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