Low-light Image Restoration with Short- and Long-exposure Raw Pairs

• URL: http://arxiv.org/abs/2007.00199v2
• Date: Sun, 28 Feb 2021 07:42:04 GMT
• Title: Low-light Image Restoration with Short- and Long-exposure Raw Pairs
• Authors: Meng Chang, Huajun Feng, Zhihai Xu, Qi Li
• Abstract summary: We propose a new low-light image restoration method by using the complementary information of short- and long-exposure images. We first propose a novel data generation method to synthesize realistic short- and longexposure raw images. Then, we design a new long-short-exposure fusion network (LSFNet) to deal with the problems of low-light image fusion.
• Score: 14.643663950015334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-light imaging with handheld mobile devices is a challenging issue. Limited by the existing models and training data, most existing methods cannot be effectively applied in real scenarios. In this paper, we propose a new low-light image restoration method by using the complementary information of short- and long-exposure images. We first propose a novel data generation method to synthesize realistic short- and longexposure raw images by simulating the imaging pipeline in lowlight environment. Then, we design a new long-short-exposure fusion network (LSFNet) to deal with the problems of low-light image fusion, including high noise, motion blur, color distortion and misalignment. The proposed LSFNet takes pairs of shortand long-exposure raw images as input, and outputs a clear RGB image. Using our data generation method and the proposed LSFNet, we can recover the details and color of the original scene, and improve the low-light image quality effectively. Experiments demonstrate that our method can outperform the state-of-the art methods.

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