Low-Light Image Enhancement
Leaderboards
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Greatest papers with code
EnlightenGAN: Deep Light Enhancement without Paired Supervision
Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data?
Deep Retinex Decomposition for Low-Light Enhancement
Based on the decomposition, subsequent lightness enhancement is conducted on illumination by an enhancement network called Enhance-Net, and for joint denoising there is a denoising operation on reflectance.
Getting to Know Low-light Images with The Exclusively Dark Dataset
Thus, we propose the Exclusively Dark dataset to elevate this data drought, consisting exclusively of ten different types of low-light images (i. e. low, ambient, object, single, weak, strong, screen, window, shadow and twilight) captured in visible light only with image and object level annotations.
Kindling the Darkness: A Practical Low-light Image Enhancer
• zhangyhuaee/KinD
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It is worth to note that our network is trained with paired images shot under different exposure conditions, instead of using any ground-truth reflectance and illumination information.
SOTA for Low-Light Image Enhancement on LOL
Attention-guided Low-light Image Enhancement
• Lvfeifan/MBLLEN
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Other contributions of this paper include a decomposition-and-fusion design of the enhancement network and the reinforcement-net for further contrast enhancement.
SOTA for Low-Light Image Enhancement on 3DMatch Benchmark (using extra training data)
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network.