Performance analysis of low-flux least-squares single-pixel imaging
Files
Accepted manuscript
Date
2016-12-01
Authors
Shin, Dongeek
Shapiro, Jeffrey H.
Goyal, Vivek K.
Version
Accepted manuscript
OA Version
Citation
Dongeek Shin, Jeffrey H Shapiro, Vivek K Goyal. 2016. "Performance Analysis of Low-Flux Least-Squares Single-Pixel Imaging." IEEE SIGNAL PROCESSING LETTERS, Volume 23, Issue 12, pp. 1756 - 1760. https://doi.org/10.1109/LSP.2016.2617329
Abstract
A single-pixel camera is able to computationally form spatially resolved images using one photodetector and a spatial light modulator. The images it produces in low-light-level operation are imperfect, even when the number of measurements exceeds the number of pixels, because its photodetection measurements are corrupted by Poisson noise. Conventional performance analysis for single-pixel imaging generates estimates of mean-square error (MSE) from Monte Carlo simulations, which require long computational times. In this letter, we use random matrix theory to develop a closed-form approximation to the MSE of the widely used least-squares inversion method for Poisson noise-limited single-pixel imaging. We present numerical experiments that validate our approximation and a motivating example showing how our framework can be used to answer practical optical design questions for a single-pixel camera.