Learning Rank-1 Diffractive Optics for Single-Shot High Dynamic Range Imaging
KAUST DepartmentComputational Imaging Group
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Visual Computing Center (VCC)
Online Publication Date2020-08-05
Print Publication Date2020-06
Permanent link to this recordhttp://hdl.handle.net/10754/662530
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AbstractHigh-dynamic range (HDR) imaging is an essential imaging modality for a wide range of applications in uncontrolled environments, including autonomous driving, robotics, and mobile phone cameras. However, existing HDR techniques in commodity devices struggle with dynamic scenes due to multi-shot acquisition and post-processing time, e.g. mobile phone burst photography, making such approaches unsuitable for real-time applications. In this work, we propose a method for snapshot HDR imaging by learning an optical HDR encoding in a single image which maps saturated highlights into neighboring unsaturated areas using a diffractive optical element (DOE). We propose a novel rank-1 parameterization of the proposed DOE which avoids vast trainable parameters and keeps high frequencies' encoding compared with conventional end-to-end design methods. We further propose a reconstruction network tailored to this rank-1 parametrization for recovery of clipped information from the encoded measurements. The proposed end-to-end framework is validated through simulation and real-world experiments and improves the PSNR by more than 7 dB over state-of-the-art end-to-end designs.
CitationSun, Q., Tseng, E., Fu, Q., Heidrich, W., & Heide, F. (2020). Learning Rank-1 Diffractive Optics for Single-Shot High Dynamic Range Imaging. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr42600.2020.00146
SponsorsThis work was supported by KAUST baseline funding.
Conference/Event name2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)