Share Email Print
cover

Proceedings Paper

Radon transform imaging: low-cost video compressive imaging at extreme resolutions
Author(s): Aswin C. Sankaranarayanan; Jian Wang; Mohit Gupta
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Most compressive imaging architectures rely on programmable light-modulators to obtain coded linear measurements of a signal. As a consequence, the properties of the light modulator place fundamental limits on the cost, performance, practicality, and capabilities of the compressive camera. For example, the spatial resolution of the single pixel camera is limited to that of its light modulator, which is seldom greater than 4 megapixels. In this paper, we describe a novel approach to compressive imaging that avoids the use of spatial light modulator. In its place, we use novel cylindrical optics and a rotation gantry to directly sample the Radon transform of the image focused on the sensor plane. We show that the reconstruction problem is identical to sparse tomographic recovery and we can leverage the vast literature in compressive magnetic resonance imaging (MRI) to good effect.

The proposed design has many important advantages over existing compressive cameras. First, we can achieve a resolution of N × N pixels using a sensor with N photodetectors; hence, with commercially available SWIR line-detectors with 10k pixels, we can potentially achieve spatial resolutions of 100 megapixels, a capability that is unprecedented. Second, our design is scalable more gracefully across wavebands of light since we only require sensors and optics that are optimized for the wavelengths of interest; in contrast, spatial light modulators like DMDs require expensive coatings to be effective in non-visible wavebands. Third, we can exploit properties of line-detectors including electronic shutters and pixels with large aspect ratios to optimize light throughput. On the ip side, a drawback of our approach is the need for moving components in the imaging architecture.

Paper Details

Date Published: 19 May 2016
PDF: 6 pages
Proc. SPIE 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 98710Q (19 May 2016); doi: 10.1117/12.2228185
Show Author Affiliations
Aswin C. Sankaranarayanan, Carnegie Mellon Univ. (United States)
Jian Wang, Carnegie Mellon Univ. (United States)
Mohit Gupta, Univ. of Wisconsin-Madison (United States)


Published in SPIE Proceedings Vol. 9871:
Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016
Liyi Dai; Yufeng Zheng; Henry Chu; Anke D. Meyer-Bäse, Editor(s)

© SPIE. Terms of Use
Back to Top