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- W4385963339 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." @default.
- W4385963339 created "2023-08-18" @default.
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- W4385963339 date "2023-08-01" @default.
- W4385963339 modified "2023-09-26" @default.
- W4385963339 title "Visibility Enhancement for Low-light Hazy Scenarios" @default.
- W4385963339 doi "https://doi.org/10.48550/arxiv.2308.00591" @default.
- W4385963339 hasPublicationYear "2023" @default.
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