Learning to Deblur Polarized Images
Chu Zhou Minggui Teng Xinyu Zhou Chao Xu Boxin Sh
A polarization camera can capture four polarized images with different
polarizer angles in a single shot, which is useful in polarization-based vision
applications since the degree of polarization (DoP) and the angle of
polarization (AoP) can be directly computed from the captured polarized images.
However, since the on-chip micro-polarizers block part of the light so that the
sensor often requires a longer exposure time, the captured polarized images are
prone to motion blur caused by camera shakes, leading to noticeable degradation
in the computed DoP and AoP. Deblurring methods for conventional images often
show degenerated performance when handling the polarized images since they only
focus on deblurring without considering the polarization constrains. In this
paper, we propose a polarized image deblurring pipeline to solve the problem in
a polarization-aware manner by adopting a divide-and-conquer strategy to
explicitly decompose the problem into two less ill-posed sub-problems, and
design a two-stage neural network to handle the two sub-problems respectively.
Experimental results show that our method achieves state-of-the-art performance
on both synthetic and real-world images, and can improve the performance of
polarization-based vision applications such as image dehazing and reflection
removal.