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Low-light Enhancement Using Retinex-Decomposition Convolutional Neural Networks
- Authors
- Sung, J.; Lim, H.; Shin, J.; Ahn, S.; Paik, Joonki
- Issue Date
- Mar-2022
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- convolutional neural network; low-light enhancement; retinex
- Citation
- Digest of Technical Papers - IEEE International Conference on Consumer Electronics, v.2022-January
- Journal Title
- Digest of Technical Papers - IEEE International Conference on Consumer Electronics
- Volume
- 2022-January
- ISSN
- 0747-668X
- Abstract
- This paper proposes a new retinex-decomposition convolutional network (DC-Net) to enhance low-light images based on retinex theory. The proposed method estimates the reflectance and illumination components using Dc-Net. Bright-Net and Smooth-Net are used for the refined illumination, and Denoise-Net returns the noise-removed reflectance. Finally, A resultant image can be estimated by multiplying the noise-removed reflectance map and brightness-improved illumination. The experimental results show that the proposed scheme can provide high-quality images without saturation. © 2022 IEEE.
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