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Poster
Learning to dehaze with polarization
Chu Zhou · Minggui Teng · Yufei Han · Chao Xu · Boxin Shi

Wed Dec 08 12:30 AM -- 02:00 AM (PST) @ Virtual
in Poster Session 3 »

Haze, a common kind of bad weather caused by atmospheric scattering, decreases the visibility of scenes and degenerates the performance of computer vision algorithms. Single-image dehazing methods have shown their effectiveness in a large variety of scenes, however, they are based on handcrafted priors or learned features, which do not generalize well to real-world images. Polarization information can be used to relieve its ill-posedness, however, real-world images are still challenging since existing polarization-based methods usually assume that the transmitted light is not significantly polarized, and they require specific clues to estimate necessary physical parameters. In this paper, we propose a generalized physical formation model of hazy images and a robust polarization-based dehazing pipeline without the above assumption or requirement, along with a neural network tailored to the pipeline. Experimental results show that our approach achieves state-of-the-art performance on both synthetic data and real-world hazy images.

Keywords: Deep Learning · Vision · Graph Learning

Author Information

Chu Zhou (Peking University)
Minggui Teng
Yufei Han (Beijing University of Posts and Telecommunications)
Chao Xu (Peking University)
Boxin Shi (Peking University)

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