Better image dehazing networks based on structural priors

Da An; Zhan Guo; Huajun Shi

doi:10.1117/12.3012079

21 December 2023 Better image dehazing networks based on structural priors

Da An, Zhan Guo, Huajun Shi

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129702S (2023) https://doi.org/10.1117/12.3012079
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Convolutional neural networks are more and more widely used in our lives. Computer vision tasks dominated by traditional algorithms are also trying to introduce deep learning networks. Image dehazing is a good field. At present, the most widely used image dehazing field is the unsupervised dehazing algorithm. By decomposing low-quality images, image dehazing (separation into clear image and haze map), it can be performed expansion operations. At present, due to the lack of paired high-quality images and hazy images in the field of image dehazing, this further intensifies the need for unsupervised dehazing algorithms. In order to further improve this problem, this article proposes an extractor DIPV1 based on a new U-Net-like structure, and based on this, combined with the biased fitting problem of the network, the existing Double-DIP structure is improved. we call it Better Double-DIP(BDouble-DIP). This network is an end-to-end network and is jointly trained by 2 DIP-V1.By stopping early, we can get better results. By applying the physical dehazing model, we can finally get a good dehazing result. We conduct experiments on the open source dataset O-Haze dataset, and compare BDoubleDIP with existing traditional algorithms: SIHR, EIDBC, supervised neural networks like DehazeNet: NTDF, NLD, URIE, and BOPBL, as well as unsupervised neural networks: DoubleDIP

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Da An, Zhan Guo, and Huajun Shi "Better image dehazing networks based on structural priors", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129702S (21 December 2023); https://doi.org/10.1117/12.3012079

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