A Two-stage Unsupervised Approach for Low light Image Enhancement
- URL: http://arxiv.org/abs/2010.09316v2
- Date: Tue, 20 Oct 2020 01:39:00 GMT
- Title: A Two-stage Unsupervised Approach for Low light Image Enhancement
- Authors: Junjie Hu, Xiyue Guo, Junfeng Chen, Guanqi Liang, Fuqin Deng and Tin
lun Lam
- Abstract summary: We propose a two-stage unsupervised method that decomposes the low light image enhancement into a pre-enhancement and a post-refinement problem.
Our method can significantly improve feature points matching and simultaneous localization and mapping in low light conditions.
- Score: 18.365345507072234
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As vision based perception methods are usually built on the normal light
assumption, there will be a serious safety issue when deploying them into low
light environments. Recently, deep learning based methods have been proposed to
enhance low light images by penalizing the pixel-wise loss of low light and
normal light images. However, most of them suffer from the following problems:
1) the need of pairs of low light and normal light images for training, 2) the
poor performance for dark images, 3) the amplification of noise. To alleviate
these problems, in this paper, we propose a two-stage unsupervised method that
decomposes the low light image enhancement into a pre-enhancement and a
post-refinement problem. In the first stage, we pre-enhance a low light image
with a conventional Retinex based method. In the second stage, we use a
refinement network learned with adversarial training for further improvement of
the image quality. The experimental results show that our method outperforms
previous methods on four benchmark datasets. In addition, we show that our
method can significantly improve feature points matching and simultaneous
localization and mapping in low light conditions.
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