A novel multi-scale fusion framework for detail-preserving low-light image enhancement

In this paper, we propose a novel multi-scale fusion framework for low-illumination image enhancement, which effectively enhances images taken under various low-light conditions. Based on the high dynamic range imaging technique, we first employ a novel remapping function to generate a sequence of artificial multi-exposure images. The generated sequence of images ensures that the contrast of each intensity interval of the input image is enhanced at least once. Then three fusion-relevant features, namely, exposure, global contrast and local contrast, are selected as the weight maps. Combined with the weight maps, a pyramid fusion scheme is introduced to do a layer-by-layer integration of the different frequency bands of the image layer by layer. In addition, a strategy for extracting details from the original image is designed, which effectively maintains the detail information without causing colour distortions. The framework is very efficient and suitable for mobile devices because most of the calculations are at the pixel-level. Extensive experiments have shown that the proposed approach yields comparable and better performances in comparisons with the state-of-the-art competing techniques in both qualitative and quantitative evaluations. (c) 2020 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a novel multi-scale fusion framework for enhancing low-illumination images by generating a sequence of artificial multi-exposure images. The framework incorporates weight maps and a pyramid fusion scheme for layer-by-layer integration of different frequency bands of the image, along with a detail extraction strategy to maintain detail information effectively. Extensive experiments have shown that the framework yields comparable and better performances compared to state-of-the-art techniques in both qualitative and quantitative evaluations.