Enhancement of Weakly Illuminated Images by Deep Fusion Networks
Yu Cheng, Jia Yan, Zhou Wang · 2019
We propose an end-to-end deep fusion-based approach to enhance the quality of images acquired in weak illumination environment. The proposed deep fusion network (DFN), without estimating illumination explicitly, uses a convolutional neural network (CNN) to generate confidence maps as spatial weighting factors to fuse images created by multiple base image enhancement techniques that complement each other in a content-dependent manner. Our tests on both synthetic and real weakly illuminated images show that the proposed DFN approach delivers superior performance in terms of both subjective visual perception and objective quality assessment.