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Semantically Contrastive Learning for Low-light Image Enhancement
[article]
2021
arXiv
pre-print
Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images. In this paper, we respond to the intriguing learning-related question -- if leveraging both accessible unpaired over/underexposed images and high-level semantic guidance, can improve the performance of cutting-edge LLE models? Here, we propose an effective semantically contrastive learning paradigm for LLE (namely SCL-LLE). Beyond the existing
arXiv:2112.06451v1
fatcat:2iri5zu6j5gltkdd4gufq7sphq