TBEFN: A Two-Branch Exposure-Fusion Network for Low-Light Image Enhancement

Images obtained under low-light conditions are usually accompanied by varied and highly unpredictable degradation. The uncertainty of the imaging environment makes the enhancement even more challenging. In this paper, we present a two-branch exposure-fusion network to tackle the problem of blind low-light image enhancement. In the first part of the paper, we provide a basic insight into the degradation mechanism of low-light images, and propose a quick and effective enhancement strategy by estimating the transfer function for varied illumination levels. To further deal with the challenge brought about by the blindness of input images, a novel generation-and-fusion strategy is then introduced, where the enhancements for slightly and heavily distorted images are carried out respectively in the two enhancing branches, followed by a self-adaptive attention unit to perform the final fusion. Moreover, a two-stage denoising strategy is also proposed to ensure effective noise reduction in a data-driven manner. To evaluate the performance of the proposed method, three commonly used datasets are adopted for quantitative evaluation and six for visual evaluation, where our method outperforms many of the existing state-of-the-art ones, showing great effectiveness and potential.