Learning to dehaze with polarization

Chu Zhou, Minggui Teng, Yufei Han, Chao Xu, Boxin Shi

Keywords: Computational photography, polarization-based image dehazing

TL;DR: In this paper, we propose a generalized physical formation model of hazy images and a robust polarization-based dehazing pipeline, along with a neural network tailored to the pipeline.

Abstract: 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.

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Code: https://github.com/fourson/Learning-to-dehaze-with-polarization