Learning to Deblur Polarized Images
This solves the problem of degraded polarization-based vision applications like dehazing and reflection removal for users of polarization cameras, but it is incremental as it adapts existing deblurring methods with polarization constraints.
The paper tackles motion blur in polarized images from polarization cameras, which degrades computed polarization parameters, by proposing a polarization-aware deblurring pipeline that uses a two-stage neural network to decompose the problem into less ill-posed sub-problems, achieving state-of-the-art performance on synthetic and real-world images.
A polarization camera can capture four linear polarized images with different polarizer angles in a single shot, which is useful in polarization-based vision applications since the degree of linear polarization (DoLP) and the angle of linear polarization (AoLP) 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 DoLP and AoLP. Deblurring methods for conventional images often show degraded performance when handling the polarized images since they only focus on deblurring without considering the polarization constraints. 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.