DeepSelfie: Single-Shot Low-Light Enhancement for Selfies
- Authors
- Lu, Yucheng; Kim, Dong-Wook; Jung, Seung-Won
- Issue Date
- 2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Deep learning; image enhancement; low-light enhancement; selfie
- Citation
- IEEE ACCESS, v.8, pp.121424 - 121436
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE ACCESS
- Volume
- 8
- Start Page
- 121424
- End Page
- 121436
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/58932
- DOI
- 10.1109/ACCESS.2020.3006525
- ISSN
- 2169-3536
- Abstract
- Taking a high-quality selfie photo in a low-light environment is challenging. Because the foreground and background often have different illumination conditions, they suffer heavily from over/under-exposure issues and cannot be treated in the same manner when applying image enhancement algorithms. In this work, we propose DeepSelfie, a learning-based image enhancement framework for low-light selfie photos. We address selfie enhancement as a dual-layer image enhancement problem. The foreground and background are thus separately enhanced and combined together via image fusion. To train the selfie enhancement network, we also introduce a method of synthesizing pairs of noisy and dark raw selfie images and their corresponding well-illuminated images. Through extensive experiments of no-reference image quality assessment as well as human subjective evaluation, we show that DeepSelfie provides better results in comparison to several state-of-the-art methods. The code and datasets can be found at https://sites.google.com/view/deepselfie.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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