A log-domain intensity-chromaticity decoupling approach for low-light image enhancement with explicit reconstruction constraints achieves 29.71 dB PSNR and 0.89 SSIM on LOLv2-Real while improving downstream face detection.
Semantic-guided zero-shot learn- ing for low-light image/video enhancement, in: Proceedings of the IEEE/CVFWinterconferenceonapplicationsofcomputervision,pp
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LiteIE proposes a two-layer backbone-agnostic feature extractor and parameter-free Iterative Restoration Module for unsupervised low-light enhancement, claiming 19.04 dB PSNR on LOL with 0.07% of SOTA parameters and 30 FPS 4K on Snapdragon 8 Gen 3.
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Rethinking Low-Light Image Enhancement: A Log-Domain Intensity--Chromaticity Decoupling Perspective
A log-domain intensity-chromaticity decoupling approach for low-light image enhancement with explicit reconstruction constraints achieves 29.71 dB PSNR and 0.89 SSIM on LOLv2-Real while improving downstream face detection.
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Towards Lightest Low-Light Image Enhancement Architecture for Mobile Devices
LiteIE proposes a two-layer backbone-agnostic feature extractor and parameter-free Iterative Restoration Module for unsupervised low-light enhancement, claiming 19.04 dB PSNR on LOL with 0.07% of SOTA parameters and 30 FPS 4K on Snapdragon 8 Gen 3.