论文标题
偏光hdr重建
Deep Polarimetric HDR Reconstruction
论文作者
论文摘要
本文提出了一种新型的基于学习摄像机的基于学习的高动力范围（HDR）重建方法。我们利用先前的观察结果，即具有不同方向的极化过滤器可以以不同的方式减弱自然光，并且我们将极化摄像机获得的多个图像视为在不同的曝光时间下获得的集合，以引入HDR重建问题的解决方案的开发。我们提出了一个深HDR重建框架，该框架具有特征掩蔽机制，该机制使用了极化摄像头可用的极化提示，称为Deep Polarimetric HDR重建（DPHR）。提出的DPHR获得了极化信息，以更有效地通过网络传播有效的特征，以回归缺失的像素。我们通过定性和定量评估证明，所提出的DPHR比最新的HDR重建算法表现出色。
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.
