Visibility Enhancement for Low-light Hazy Scenarios
Chaoqun Zhuang Yunfei Liu Sijia Wen Feng Lu
Low-light hazy scenes commonly appear at dusk and early morning. The visual
enhancement for low-light hazy images is an ill-posed problem. Even though
numerous methods have been proposed for image dehazing and low-light
enhancement respectively, simply integrating them cannot deliver pleasing
results for this particular task. In this paper, we present a novel method to
enhance visibility for low-light hazy scenarios. To handle this challenging
task, we propose two key techniques, namely cross-consistency
dehazing-enhancement framework and physically based simulation for low-light
hazy dataset. Specifically, the framework is designed for enhancing visibility
of the input image via fully utilizing the clues from different sub-tasks. The
simulation is designed for generating the dataset with ground-truths by the
proposed low-light hazy imaging model. The extensive experimental results show
that the proposed method outperforms the SOTA solutions on different metrics
including SSIM (9.19%) and PSNR(5.03%). In addition, we conduct a user study on
real images to demonstrate the effectiveness and necessity of the proposed
method by human visual perception.