Abstract

This paper proposes a self-supervised polarization image dehazing method with an angle-of-polarization (AoP) frequency-domain prior for strong scattering dense-haze scenarios. The method formulates dehazing as the recovery of the clear object-radiance polarization field, rather than only restoring a haze-free intensity image. By analyzing real polarized hazy images, we observe that atmospheric AoP is dominated by low-frequency components, while object-radiance AoP contains richer local variations. Based on this observation, an AoP frequency-domain prior is incorporated into the polarization scattering model to guide the separation of object radiance and atmospheric polarization. A two-stage self-supervised training framework is then developed, where physical priors and the AoP prior provide stable component estimates, followed by joint optimization through scattering reconstruction consistency. In the object-radiance branch, a spatial-frequency dual-domain enhancement module is designed to capture both global haze degradation and local structural details. Experiments on a self-collected real short-wave infrared polarized hazy image dataset demonstrate that the proposed method achieves better target visibility, structural restoration, and quantitative performance than existing methods under dense-haze and strong scattering conditions.

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