Polarized color photography provides both visual textures and object
surficial information in one single snapshot. However, the use of the
directional polarizing filter array causes extremely lower photon count and SNR
compared to conventional color imaging. Thus, the feature essentially leads to
unpleasant noisy images and destroys polarization analysis performance. It is a
challenge for traditional image processing pipelines owing to the fact that the
physical constraints exerted implicitly in the channels are excessively
complicated. To address this issue, we propose a learning-based approach to
simultaneously restore clean signals and precise polarization information. A
real-world polarized color image dataset of paired raw short-exposed noisy and
long-exposed reference images are captured to support the learning-based
pipeline. Moreover, we embrace the development of vision Transformer and
propose a hybrid transformer model for the Polarized Color image denoising,
namely PoCoformer, for a better restoration performance. Abundant experiments
demonstrate the effectiveness of proposed method and key factors that affect
results are analyzed.

本文提出了基于学习的方法来同时恢复清晰的信号和精确的极化信息，一个基于视觉 Transformer 的混合 Transformer 模型被提出以进行极化彩色图像去噪，实验表明该方法的有效性并分析了影响结果的关键因素。