Fixed point simulation of compressed sensing and reconstruction

Pravir Singh Gupta; Gwan Seong Choi

doi:10.1117/12.2520633

13 May 2019 Fixed point simulation of compressed sensing and reconstruction

Pravir Singh Gupta, Gwan Seong Choi

Proceedings Volume 10990, Computational Imaging IV; 109900I (2019) https://doi.org/10.1117/12.2520633
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Citation Download Citation

Pravir Singh Gupta and Gwan Seong Choi "Fixed point simulation of compressed sensing and reconstruction", Proc. SPIE 10990, Computational Imaging IV, 109900I (13 May 2019); https://doi.org/10.1117/12.2520633

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