Low-Light Image Enhancement by Principal Component Analysis
Under extreme low-lighting conditions, images have low contrast, low brightness, and high noise. In this paper, we propose a principal component analysis framework to enhance low-light-level images with decomposed luminance–chrominance components. A multi-scale retinex-based adaptive filt...
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doaj-26abf314b6f64477bb485c93fb2873e62021-03-29T22:10:17ZengIEEEIEEE Access2169-35362019-01-0173082309210.1109/ACCESS.2018.28872968580556Low-Light Image Enhancement by Principal Component AnalysisSteffi Agino Priyanka0Yuan-Kai Wang1https://orcid.org/0000-0002-0676-5886Shih-Yu Huang2Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City, TaiwanDepartment of Electrical Engineering, Fu Jen Catholic University, New Taipei City, TaiwanDepartment of Information and Communication Engineering, Ming Chuan University, Taoyuan, TaiwanUnder extreme low-lighting conditions, images have low contrast, low brightness, and high noise. In this paper, we propose a principal component analysis framework to enhance low-light-level images with decomposed luminance–chrominance components. A multi-scale retinex-based adaptive filter is developed for the luminance component to enhance contrast and brightness significantly. Noise is attenuated by a proposed collaborative filtering employed to both the luminance and chrominance components that reveal every finest detail by preserving the unique features in the image. To evaluate the effectiveness of the proposed algorithm, a simulation model is proposed to generate nighttime images for various levels of contrast and noise. The proposed algorithm can process a wide range of images without introducing ghosting and halo artifacts. The quantitative performance of the algorithm is measured in terms of both full-reference and blind performance metrics. It shows that the proposed method delivers state-of-the-art performance both in terms of objective criteria and visual quality compared to the existing methods.https://ieeexplore.ieee.org/document/8580556/Contrast enhancementdenoisingprincipal component analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Steffi Agino Priyanka Yuan-Kai Wang Shih-Yu Huang |
spellingShingle |
Steffi Agino Priyanka Yuan-Kai Wang Shih-Yu Huang Low-Light Image Enhancement by Principal Component Analysis IEEE Access Contrast enhancement denoising principal component analysis |
author_facet |
Steffi Agino Priyanka Yuan-Kai Wang Shih-Yu Huang |
author_sort |
Steffi Agino Priyanka |
title |
Low-Light Image Enhancement by Principal Component Analysis |
title_short |
Low-Light Image Enhancement by Principal Component Analysis |
title_full |
Low-Light Image Enhancement by Principal Component Analysis |
title_fullStr |
Low-Light Image Enhancement by Principal Component Analysis |
title_full_unstemmed |
Low-Light Image Enhancement by Principal Component Analysis |
title_sort |
low-light image enhancement by principal component analysis |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Under extreme low-lighting conditions, images have low contrast, low brightness, and high noise. In this paper, we propose a principal component analysis framework to enhance low-light-level images with decomposed luminance–chrominance components. A multi-scale retinex-based adaptive filter is developed for the luminance component to enhance contrast and brightness significantly. Noise is attenuated by a proposed collaborative filtering employed to both the luminance and chrominance components that reveal every finest detail by preserving the unique features in the image. To evaluate the effectiveness of the proposed algorithm, a simulation model is proposed to generate nighttime images for various levels of contrast and noise. The proposed algorithm can process a wide range of images without introducing ghosting and halo artifacts. The quantitative performance of the algorithm is measured in terms of both full-reference and blind performance metrics. It shows that the proposed method delivers state-of-the-art performance both in terms of objective criteria and visual quality compared to the existing methods. |
topic |
Contrast enhancement denoising principal component analysis |
url |
https://ieeexplore.ieee.org/document/8580556/ |
work_keys_str_mv |
AT steffiaginopriyanka lowlightimageenhancementbyprincipalcomponentanalysis AT yuankaiwang lowlightimageenhancementbyprincipalcomponentanalysis AT shihyuhuang lowlightimageenhancementbyprincipalcomponentanalysis |
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