MTRBNet: Multi-Branch Topology Residual Block-Based Network for Low-Light Enhancement
Author:
Affiliation:
1. School of Navigation, Wuhan University of Technology, Wuhan, China
2. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
Funder
Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/97/9686799/09741357.pdf?arnumber=9741357
Reference49 articles.
1. A bio-inspired multi-exposure fusion framework for low-light image enhancement;ying,2017
2. Lightening Network for Low-Light Image Enhancement
3. Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement
4. Degrade is upgrade: Learning degradation for low-light image enhancement;jiang,2021
5. LE-GAN: Unsupervised low-light image enhancement network using attention module and identity invariant loss
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