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Proceedings Paper

Fixed point simulation of compressed sensing and reconstruction
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Paper Abstract

This work presents a fixed point simulation of Compressed Sensing (CS) and reconstruction for Super-Resolution task using Image System Engineering Toolbox (ISET). This work shows that performance of CS for super- resolution in fixed point implementation is similar to floating point implementation and there is negligible loss in reconstruction quality. It also shows that CS Super-Resolution requires much less computation effort compared to CS using Gaussian Random matrices. Additionally, it also studies the effect of Analog-to-Digital-Converter (ADC) bitwidth and image sensor noise on reconstruction performance. CS super-resolution cuts the raw data bits generated from image sensor by more than half and conversion of reconstruction algorithm to fixed point allows one to simplify the hardware implementation by replacing expensive floating point computational units with faster and energy efficient fixed point units.

Paper Details

Date Published: 13 May 2019
PDF: 10 pages
Proc. SPIE 10990, Computational Imaging IV, 109900I (13 May 2019); doi: 10.1117/12.2520633
Show Author Affiliations
Pravir Singh Gupta, Texas A&M Univ. (United States)
Gwan Seong Choi, Texas A&M Univ. (United States)

Published in SPIE Proceedings Vol. 10990:
Computational Imaging IV
Abhijit Mahalanobis; Lei Tian; Jonathan C. Petruccelli, Editor(s)

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