Deep Polarimetric HDR Reconstruction
This work addresses HDR reconstruction for imaging applications by introducing a novel use of polarization data, though it appears incremental as it builds on existing observations about polarization filters.
The paper tackles high-dynamic-range (HDR) reconstruction by using a polarization camera to capture multiple images treated as different exposures, proposing a deep learning framework called DPHR that leverages polarimetric cues for feature masking. The result shows that DPHR performs favorably compared to state-of-the-art HDR algorithms in qualitative and quantitative evaluations.
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.