Single Image Dehazing via Model-Based Deep-Learning

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Single Image Dehazing via Model-Based Deep-Learning
Title:
Single Image Dehazing via Model-Based Deep-Learning
Journal Title:
2022 IEEE International Conference on Image Processing (ICIP)
Publication Date:
18 October 2022
Citation:
Z. Li, C. Zheng, H. Shu and S. Wu, & Single Image Dehazing via Model-Based Deep-Learning, 2022 IEEE International Conference on Image Processing (ICIP), 2022, pp. 141-145, doi: 10.1109/ICIP46576.2022.9897479.
Abstract:
Model-based single image dehazing algorithms restore images with sharp edges and rich details at the expense of low PSNR values. Data-driven ones restore images with high PSNR values but with low contrast, and even some remaining haze. In this paper, a novel single image dehazing algorithm is introduced by integrating model-based and data-driven approaches. Both transmission map and atmospheric light are initialized by the model-based methods, and refined by deep learning based approaches which form a neural augmentation. Haze-free images are restored by using the transmission map and atmospheric light. Experimental results indicate that the proposed algorithm can remove haze well from real-world and synthetic hazy images.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISBN:
978-1-6654-9620-9
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