Learning Rank-1 Diffractive Optics for Single-Shot High Dynamic Range Imaging
Type
Conference PaperKAUST Department
Computational Imaging GroupComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering
Electrical Engineering Program
Visual Computing Center (VCC)
Date
2020-08-05Online Publication Date
2020-08-05Print Publication Date
2020-06Permanent link to this record
http://hdl.handle.net/10754/662530
Metadata
Show full item recordAbstract
High-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.Citation
Sun, 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/cvpr42600.2020.00146Sponsors
This work was supported by KAUST baseline funding.Conference/Event name
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)ISBN
978-1-7281-7169-2Additional Links
https://ieeexplore.ieee.org/document/9157825/https://ieeexplore.ieee.org/document/9157825/
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9157825
ae974a485f413a2113503eed53cd6c53
10.1109/CVPR42600.2020.00146