Deep Snapshot HDR Reconstruction Based on the Polarization Camera

TL;DR

This paper introduces a deep learning-based method for HDR imaging using polarization cameras, leveraging the polarization-dependent attenuation of light to reconstruct high-dynamic-range images from multiple polarization images.

Contribution

It proposes a novel deep snapshot HDR reconstruction framework utilizing polarization images, along with a polarized HDR dataset for training and evaluation.

Findings

Outperforms state-of-the-art HDR algorithms

Effectively models polarization-light relationships

Creates a new polarized HDR dataset

Abstract

The recent development of the on-chip micro-polarizer technology has made it possible to acquire four spatially aligned and temporally synchronized polarization images with the same ease of operation as a conventional camera. In this paper, we investigate the use of this sensor technology in high-dynamic-range (HDR) imaging. Specifically, observing that natural light can be attenuated differently by varying the orientation of the polarization filter, we treat the multiple images captured by the polarization camera as a set captured under different exposure times. In our approach, we first study the relationship among polarizer orientation, degree and angle of polarization of light to the exposure time of a pixel in the polarization image. Subsequently, we propose a deep snapshot HDR reconstruction framework to recover an HDR image using the polarization images. A polarized HDR dataset is created to train and evaluate our approach. We demonstrate that our approach performs favorably against state-of-the-art HDR reconstruction algorithms.