Visibility Enhancement for Low-light Hazy Scenarios
- URL: http://arxiv.org/abs/2308.00591v1
- Date: Tue, 1 Aug 2023 15:07:38 GMT
- Title: Visibility Enhancement for Low-light Hazy Scenarios
- Authors: Chaoqun Zhuang, Yunfei Liu, Sijia Wen, Feng Lu
- Abstract summary: Low-light hazy scenes commonly appear at dusk and early morning.
We propose a novel method to enhance visibility for low-light hazy scenarios.
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.
- Score: 18.605784907840473
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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