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arXiv Computer Vision By Chenggong Li, Jinhao Liu, Caiyun Wu, Yidong Luo, Junchao Zhang, Degui Yang

Learning Polarization Image Restoration with General Restoration Priors

Read the original on arXiv Computer Vision →

The paper presents an all-in-one framework for restoring polarization images affected by multiple coupled degradations. It identifies the normalized Stokes representation as effective for separating intensity and polarization, and introduces a dual-branch architecture that uses pretrained general restoration priors for the intensity branch while transferring knowledge to the polarization branch via cross-domain feature transform. A composite-degradation benchmark is also established to support future research.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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