Abstract

In order to solve the problems of color shift and incomplete dehazing after image dehazing, this paper proposes an improved image self-supervised learning dehazing algorithm that combines polarization characteristics and deep learning. First, based on the YOLY network framework, a multiscale module and an attention mechanism module are introduced into the transmission feature estimation network. This enables the extraction of feature information at different scales and allocation of weights, and effectively improves the accuracy of transmission map estimation. Second, a brightness consistency loss based on the YCbCr color space and a color consistency loss are proposed to constrain the brightness and color consistency of the dehazing results, resolving the problems of darkened brightness and color shifts in dehazed images. Finally, the network is trained to dehaze polarized images based on the atmospheric scattering model and loss function constraints. Experiments are conducted on synthetic and real-world data, and comparisons are made with six contrasting dehazing algorithms. The results demonstrate that, compared to the contrastive dehazing algorithms, the proposed algorithm achieves PSNR and SSIM values of 23.92 and 0.94, respectively, on synthetic image samples. For real-world image samples, color restoration is more authentic, contrast is higher, and detailed information is richer. Both subjective and objective evaluations show significant improvements. This validates the effectiveness and superiority of the proposed dehazing algorithm.

© 2024 Optica Publishing Group

Image dehazing algorithm based on optimized dark channel and haze-line priors of adaptive sky segmentation

Guangmang Cui, Qiong Ma, Jufeng Zhao, Shunjie Yang, and Ziyi Chen
J. Opt. Soc. Am. A 40(6) 1165-1182 (2023)

Haze optical-model-based nighttime image dehazing by modifying attenuation and atmospheric light

Sen Lin, Penghui Sun, Hongwei Gao, and Zhaojie Ju
J. Opt. Soc. Am. A 39(10) 1893-1902 (2022)

Unsupervised single-image dehazing using the multiple-scattering model

Shunmin An, Xixia Huang, Linling Wang, Zhangjing Zheng, and Le Wang
Appl. Opt. 60(26) 7858-7868 (2021)

Reliable image dehazing by NeRF

Zheyan Jin, Zhihai Xu, Huajun Feng, Qi Li, and Yueting Chen
Opt. Express 32(3) 3528-3550 (2024)

Single image dehazing algorithm based on optical diffraction deep neural networks

Mingzhu Song, Runze Li, Rong Guo, Gege Ding, Yuezhu Wang, and Junsheng Wang
Opt. Express 30(14) 24394-24406 (2022)