Fast and accurate single pixel imaging using estimation uncertainty in explainable CNNs

Abstract

Single-pixel imaging, which allows imaging with a single-pixel detector and a correlation method, can be accelerated by combining machine learning. In addition, the accuracy of the estimation was improved using the uncertainty of the estimated value by machine learning. The machine-learning algorithm was constructed from a physical perspective based on errors in the measurement system. On the other hand, to improve the reliability of the machine learning estimates, the uncertainty of the estimates was evaluated using standard deviation values derived by data augmentation. By using the value with the lowest uncertainty as the final estimate, we improved machine learning and achieved measurements with a small number of illuminations.