Low-Light Image Restoration With Short- and Long-Exposure Raw Pairs

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 long-exposure raw images by simulating the imaging pipeline in low-light 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 short- and 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.

I. Introduction

Imaging in a low-light environment is always a challenging subject, especially on mobile devices. Limited by the quality of optical imaging devices on these devices, images captured in low light suffer from high level noise, low visualization and color distortion when the exposure time is short. One way to reduce noise and obtain accurate color is extending the exposure time with sensor sensitivity reduction. However, due to the camera motion of handheld devices or object motion in the scenes, the resulting long-exposure images suffer from motion blur. In addition, many highlighted areas may have a large area of overexposure, causing the dynamic range of the image to be cut off. Therefore, to obtain high-quality images in dark scenes, image postprocessing is often required.