Fusion-Based Low-Light Image Enhancement

Recently, deep learning-based methods have made remarkable progress in low-light image enhancement. In addition to poor contrast, the images captured under insufficient light suffer from severe noise and saturation distortion. Most existing unsupervised learning-based methods adopt the two-stage processing method to enhance contrast and denoise sequentially. However, the noise will be amplified in the contrast enhancement process, thus increasing the difficulty of denoising. Besides, the saturation distortion caused by insufficient illumination is not considered well in existing unsupervised low-light enhancement methods. To address the above problems, we propose a novel parallel framework, which includes a saturation adaptive adjustment branch, brightness adjustment branch, noise suppression branch, and fusion module for adjusting saturation, correcting brightness, denoise, and multi-branch fusion, respectively. Specifically, the saturation is corrected via global adjustment, the contrast is enhanced through curve mapping estimation, and we use BM3D to preliminary denoise. Further, the enhanced branches are fed to the fusion module for a trainable guided filter, which is optimized in an unsupervised training manner. Experiments on the LOL, MIT-Adobe 5k, and SICE datasets demonstrate that our method achieves better quantitation and qualification results than the state-ofthe-art algorithms.