  This paper proposes a novel learning based high-dynamic-range (HDR)
reconstruction method using a polarization camera. We utilize a previous
observation that polarization filters with different orientations can attenuate
natural light differently, and we treat the multiple images acquired by the
polarization camera as a set acquired under different exposure times, to
introduce the development of solutions for the HDR reconstruction problem. We
propose a deep HDR reconstruction framework with a feature masking mechanism
that uses polarimetric cues available from the polarization camera, called Deep
Polarimetric HDR Reconstruction (DPHR). The proposed DPHR obtains polarimetric
information to propagate valid features through the network more effectively to
regress the missing pixels. We demonstrate through both qualitative and
quantitative evaluations that the proposed DPHR performs favorably than
state-of-the-art HDR reconstruction algorithms.
