Improving image quality in compressed ultrafast photography with a space- and intensity-constrained reconstruction algorithm

Liren Zhu; Yujia Chen; Jinyang Liang; Liang Gao; Cheng Ma; Lihong V. Wang

doi:10.1117/12.2215017

7 March 2016 Improving image quality in compressed ultrafast photography with a space- and intensity-constrained reconstruction algorithm

Liren Zhu, Yujia Chen, Jinyang Liang, Liang Gao, Cheng Ma, Lihong V. Wang

Author Affiliations +

Proceedings Volume 9720, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management; 972008 (2016) https://doi.org/10.1117/12.2215017
Event: SPIE BiOS, 2016, San Francisco, California, United States

Abstract

The single-shot compressed ultrafast photography (CUP) camera is the fastest receive-only camera in the world. In this work, we introduce an external CCD camera and a space- and intensity-constrained (SIC) reconstruction algorithm to improve the image quality of CUP. The CCD camera takes a time-unsheared image of the dynamic scene. Unlike the previously used unconstrained algorithm, the proposed algorithm incorporates both spatial and intensity constraints, based on the additional prior information provided by the external CCD camera. First, a spatial mask is extracted from the time-unsheared image to define the zone of action. Second, an intensity threshold constraint is determined based on the similarity between the temporally projected image of the reconstructed datacube and the time-unsheared image taken by the external CCD. Both simulation and experimental studies showed that the SIC reconstruction improves the spatial resolution, contrast, and general quality of the reconstructed image.

Conference Presentation

Citation Download Citation

Liren Zhu, Yujia Chen, Jinyang Liang, Liang Gao, Cheng Ma, and Lihong V. Wang "Improving image quality in compressed ultrafast photography with a space- and intensity-constrained reconstruction algorithm", Proc. SPIE 9720, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management, 972008 (7 March 2016); https://doi.org/10.1117/12.2215017

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