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|Abstract:||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, Qilin, Ethan Tseng, Qiang Fu, Wolfgang Heidrich, and Felix Heide. "Learning Rank-1 Diffractive Optics for Single-Shot High Dynamic Range Imaging." In IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020): pp. 1383-1393. doi:10.1109/CVPR42600.2020.00146|
|Pages:||1383 - 1393|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||IEEE/CVF Conference on Computer Vision and Pattern Recognition|
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