Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement
Yuhui Wu, Chen Pan, Guoqing Wang, Yang Yang, Jiwei Wei, Chongyi Li, Heng Tao Shen
Abstract
LLFlow-L-SKF(Ours) LLFlow-S-SKF(Ours) LLFlow-S(AAAI 22) DRBN-SKF(Ours) KinD++-SKF(Ours) DRBN(CVPR 20) HWMNet(ICIP 22) HWMNet-SKF(Ours) DRBN(CVPR 20) KinD++(IJCV 20) LLFlow-S(AAAI 22) LLFlow-L (AAAI 22) KinD++-SKF(Ours) DRBN-SKF(Ours) LLFlow-L-SKF(Ours) LLFlow-S-SKF(Ours) HWMNet(ICIP 22) HWMNet-SKF(Ours) LLFlow-L(AAAI 22) Low-light Normal-light w/o Semantic w/ Semantic (a) Visual comparison on various scenes including car, human and sky. (b) Performance comparison on LOL/LOL-v2 (left/right) datasets KinD++(IJCV 20) Low-light Normal-light Low-light Normal-light w/ Semantic w/o Semantic w/ Semantic w/o Semantic SNR-Net-SKF(Ours) SNR-Net(CVPR 22) SNR-Net-SKF(Ours) SNR-Net(CVPR 22) Figure 1. Motivation and superiority. (a) The enhancement results (bottom row) without semantic priors show color deviations (e.g., the black car turns gray). (b) Our SKF provides remarkable performance boost on LOL/LOL-v2 datasets in terms of PSNR/SSIM.
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Install the CLIlune papers fulltext 3ef0198d-091e-407e-b60e-d4a5848f0549Cited by top-tier papers41
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