Quantization-aware Deep Optics for Diffractive Snapshot Hyperspectral Imaging

Lingen Li, Lizhi Wang*, Weitao Song, Lei Zhang, Zhiwei Xiong, Hua Huang

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

25 引用 (Scopus)

摘要

Diffractive snapshot hyperspectral imaging based on the deep optics framework has been striving to capture the spectral images of dynamic scenes. However, existing deep optics frameworks all suffer from the mismatch between the optical hardware and the reconstruction algorithm due to the quantization operation in the diffractive optical element (DOE) fabrication, leading to the limited performance of hyperspectral imaging in practice. In this paper, we propose the quantization-aware deep optics for diffractive snapshot hyperspectral imaging. Our key observation is that common lithography techniques used in fabricating DOEs need to quantize the DOE height map to a few levels, and can freely set the height for each level. Therefore, we propose to integrate the quantization operation into the DOE height map optimization and design an adaptive mechanism to adjust the physical height of each quantization level. According to the optimization, we fabricate the quantized DOE directly and build a diffractive hyperspectral snapshot imaging system. Our method develops the deep optics framework to be more practical through the awareness of and adaptation to the quantization operation of the DOE physical structure, making the fabricated DOE and the reconstruction algorithm match each other systematically. Extensive synthetic simulation and real hardware experiments validate the superior performance of our method.

源语言英语
主期刊名Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
出版商IEEE Computer Society
19748-19757
页数10
ISBN(电子版)9781665469463
DOI
出版状态已出版 - 2022
活动2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, 美国
期限: 19 6月 202224 6月 2022

出版系列

姓名Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2022-June
ISSN(印刷版)1063-6919

会议

会议2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
国家/地区美国
New Orleans
时期19/06/2224/06/22

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