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

Yasuhiro Mizutani; Shoma Kataoka; Tsutomu Uenohara; Yasuhiro Takaya; Osamu Matoba

doi:10.1117/12.3002270

13 March 2024 Fast and accurate single pixel imaging using estimation uncertainty in explainable CNNs

Yasuhiro Mizutani, Shoma Kataoka, Tsutomu Uenohara, Yasuhiro Takaya, Osamu Matoba

Proceedings Volume 12903, AI and Optical Data Sciences V; 129030F (2024) https://doi.org/10.1117/12.3002270
Event: SPIE OPTO, 2024, San Francisco, California, United States

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yasuhiro Mizutani, Shoma Kataoka, Tsutomu Uenohara, Yasuhiro Takaya, and Osamu Matoba "Fast and accurate single pixel imaging using estimation uncertainty in explainable CNNs", Proc. SPIE 12903, AI and Optical Data Sciences V, 129030F (13 March 2024); https://doi.org/10.1117/12.3002270

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