Dual-Scale Single Image Dehazing via Neural Augmentation

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Dual-Scale Single Image Dehazing via Neural Augmentation
Title:
Dual-Scale Single Image Dehazing via Neural Augmentation
Journal Title:
IEEE Transactions on Image Processing
Keywords:
Publication Date:
23 September 2022
Citation:
Li, Z., Zheng, C., Shu, H., & Wu, S. (2022). Dual-Scale Single Image Dehazing via Neural Augmentation. IEEE Transactions on Image Processing, 31, 6213–6223. https://doi.org/10.1109/tip.2022.3207571
Abstract:
Model-based single image dehazing algorithms restore haze-free images with sharp edges and rich details for real-world hazy images at the expense of low PSNR and SSIM values for synthetic hazy images. Data-driven ones restore haze-free images with high PSNR and SSIM values for synthetic hazy images but with low contrast, and even some remaining haze for real-world hazy images. In this paper, a novel single image dehazing algorithm is introduced by combining model-based and data-driven approaches. Both transmission map and atmospheric light are first estimated by the model-based methods, and then refined by dual-scale generative adversarial networks (GANs)based approaches. The resultant algorithm forms a neural augmentation which converges very fast while the corresponding data-driven approach might not converge. Haze-free images are restored by using the estimated transmission map and atmospheric light as well as the Koschmieder’s law. Experimental results indicate that the proposed algorithm can remove haze well from real-world and synthetic hazy images.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AI3 HTPO Seed Fund (AHSF)
Grant Reference no. : C211118005
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.
ISSN:
1057-7149
1941-0042
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