Pol2Pol: self-supervised polarimetric image denoising

Abstract

In this Letter, we present a self-supervised method, polarization to polarization (Pol2Pol), for polarimetric image denoising with only one-shot noisy images. First, a polarization generator is proposed to generate training image pairs, which are synthesized from one-shot noisy images by exploiting polarization relationships. Second, the Pol2Pol method is extensible and compatible, and any network that performs well in supervised image denoising tasks can be deployed to Pol2Pol after proper modifications. Experimental results show Pol2Pol outperforms other self-supervised methods and achieves comparable performance to supervised methods.