Quality-Improved and Property-Preserved Polarimetric Imaging via Complementarily Fusing
Chu Zhou, Yixing Liu, Chao Xu, Boxin Shi
Abstract
Polarimetric imaging is a challenging problem in the field of polarization-based vision, since setting a short exposure time reduces the signal-to-noise ratio, making the degree of polarization (DoP) and the angle of polarization (AoP) severely degenerated, while if setting a relatively long exposure time, the DoP and AoP would tend to be over-smoothed due to the frequently-occurring motion blur. This work proposes a polarimetric imaging framework that can produce clean and clear polarized snapshots by complementarily fusing a degraded pair of noisy and blurry ones. By adopting a neural network-based three-phase fusing scheme with specially-designed modules tailored to each phase, our framework can not only improve the image quality but also preserve the polarization properties. Experimental results show that our framework achieves state-of-the-art performance.
BibTeX
@inproceedings{
zhou2024qualityimproved,
title={Quality-Improved and Property-Preserved Polarimetric Imaging via Complementarily Fusing},
author={Chu Zhou and Yixing Liu and Chao Xu and Boxin Shi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mOK4yD8JFd}
}