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.
Deep Illumination-Aware Dehazing With Low-Light and Detail Enhancement
IEEE Transactions on Intelligent Transportation Systems ; 23 , 3 ; 2494-2508
2022-03-01
6952514 byte
Article (Journal)
Electronic Resource
English
Detail Maintained Low-light Video Image Enhancement Algorithm
British Library Conference Proceedings | 2018
|Local Enhancement Contrast Method for Maritime Tugboat Image Dehazing
Springer Verlag | 2025
|Springer Verlag | 2021
|