PIDSR: Complementary Polarized Image Demosaicing and Super-Resolution
Abstract
Polarization cameras can capture multiple polarized images with different polarizer angles in a single shot, bringing convenience to polarization-based downstream tasks. However, their direct outputs are color-polarization filter array (CPFA) raw images, requiring demosaicing to reconstruct full-resolution, full-color polarized images; unfortunately, this necessary step introduces artifacts that make polarization-related parameters such as the degree of polarization (DoP) and angle of polarization (AoP) prone to error. Besides, limited by the hardware design, the resolution of a polarization camera is often much lower than that of a conventional RGB camera. Existing polarized image demosaicing (PID) methods are limited in that they cannot enhance resolution, while polarized image super-resolution (PISR) methods, though designed to obtain high-resolution (HR) polarized images from the demosaicing results, tend to retain or even amplify errors in the DoP and AoP introduced by demosaicing artifacts. In this paper, we propose PIDSR, a joint framework that performs complementary Polarized Image Demosaicing and Super-Resolution, showing the ability to robustly obtain high-quality HR polarized images with more accurate DoP and AoP from a CPFA raw image in a direct manner. Experiments show our PIDSR not only achieves state-of-the-art performance on both synthetic and real data, but also facilitates downstream tasks.
Cite
Text
Zhou et al. "PIDSR: Complementary Polarized Image Demosaicing and Super-Resolution." Conference on Computer Vision and Pattern Recognition, 2025. doi:10.1109/CVPR52734.2025.01499Markdown
[Zhou et al. "PIDSR: Complementary Polarized Image Demosaicing and Super-Resolution." Conference on Computer Vision and Pattern Recognition, 2025.](https://mlanthology.org/cvpr/2025/zhou2025cvpr-pidsr/) doi:10.1109/CVPR52734.2025.01499BibTeX
@inproceedings{zhou2025cvpr-pidsr,
title = {{PIDSR: Complementary Polarized Image Demosaicing and Super-Resolution}},
author = {Zhou, Shuangfan and Zhou, Chu and Lyu, Youwei and Guo, Heng and Ma, Zhanyu and Shi, Boxin and Sato, Imari},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2025},
pages = {16081-16090},
doi = {10.1109/CVPR52734.2025.01499},
url = {https://mlanthology.org/cvpr/2025/zhou2025cvpr-pidsr/}
}