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http://dx.doi.org/10.9717/kmms.2022.25.8.991

Low-Light Invariant Video Enhancement Scheme Using Zero Reference Deep Curve Estimation  

Choi, Hyeong-Seok (Department of Information and Telecommication Eng. The University of Suwon)
Yang, Yoon Gi (Department of Information and Telecommication Eng. The University of Suwon)
Publication Information
Journal of Korea Multimedia Society / v.25, no.8, 2022 , pp. 991-998 More about this Journal
Abstract
Recently, object recognition using image/video signals is rapidly spreading on autonomous driving and mobile phones. However, the actual input image/video signals are easily exposed to a poor illuminance environment. A recent researches for improving illumination enable to estimate and compensate the illumination parameters. In this study, we propose VE-DCE (video enhancement zero-reference deep curve estimation) to improve the illumination of low-light images. The proposed VE-DCE uses unsupervised learning-based zero-reference deep curve, which is one of the latest among learning based estimation techniques. Experimental results show that the proposed method can achieve the quality of low-light video as well as images compared to the previous method. In addition, it can reduce the computational complexity with respect to the existing method.
Keywords
Low Light Image; Image Enhancement; Video Processing; Fast Algorithm; Deep Learning;
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