Metrics for Low-Light Image Quality Assessment (저조도 영상의 품질 평가를 위한 지표 제안)

Sangmin Kim (김상민)

doi:10.9708/jksci.2023.28.08.011

Metrics for Low-Light Image Quality Assessment

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2023, 28(8), pp.11-19
DOI : 10.9708/jksci.2023.28.08.011
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : July 11, 2023
Accepted : August 1, 2023
Published : August 31, 2023

Sangmin Kim ¹

¹ENERZAi

Accredited

ABSTRACT

In this paper, it is confirmed that the metrics used to evaluate image quality can be applied to low-light images. Due to the nature of low-illumination images, factors related to light create various noise patterns, and the smaller the amount of light, the more severe the noise. Therefore, in situations where it is difficult to obtain a clean image without noise, the quality of a low-illuminance image from which noise has been removed is often judged by the human eye. In this paper, noise in low-illuminance images for which ground truth cannot be obtained is removed using Noise2Noise, and spatial resolution and radial resolution are evaluated using ISO 12233 charts and colorchecker as metrics such as MTF and SNR. It can be shown that the quality of the low-illuminance image, which has been evaluated mainly for qualitative evaluation, can also be evaluated quantitatively.

KEYWORDS

Low light enhancement, Denoising, Noise2Noise, Deep learning, MTF, SNR

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Metrics for Low-Light Image Quality Assessment

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Metrics for Low-Light Image Quality Assessment

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.