Deep Illumination-Aware Dehazing With Low-Light and Detail Enhancement
Guisik Kim, Junseok Kwon
Abstract
We present a novel dehazing framework for real-world images that contain both hazy and low-light areas. Dehazing and low-light enhancements are unified by using an illumination map that is estimated using a proposed convolutional neural network. The illumination map is then used as a component for three different tasks: atmospheric light estimation, transmission map estimation, and low-light enhancement, thereby enabling the solving of interrelated low-level vision problems simultaneously. To train the neural network to perform both dehazing and low-light enhancement, we synthesize hazy and low-light images from normal images. Experimental results demonstrate that the proposed method quantitatively and qualitatively outperforms state-of-the-art algorithms in real-world image dehazing.