Normalized Ghost Imaging
Baoqing Sun, Stephen S. Welsh, Matthew P. Edgar, Jeffrey H. Shapiro,, and Miles J. Padgett
TL;DR
This paper experimentally compares various iterative ghost imaging algorithms, introducing a normalized weighting method that improves performance by accounting for light field efficiency variations.
Contribution
It proposes a normalized weighting algorithm for ghost imaging that enhances performance and matches differential ghost imaging results.
Findings
Normalized weighting improves ghost imaging accuracy
Performance matches differential ghost imaging
Experimental validation with spatial light modulator
Abstract
We present an experimental comparison between different iterative ghost imaging algorithms. Our experimental setup utilizes a spatial light modulator for generating known random light fields to illuminate a partially-transmissive object. We adapt the weighting factor used in the traditional ghost imaging algorithm to account for changes in the efficiency of the generated light field. We show that our normalized weighting algorithm can match the performance of differential ghost imaging.
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