Prediction uncertainty evaluation of deep learning ghost imaging based on the feature map

S. Kataoka; Y. Mizutani; T. Uenohara; Y. Takaya

doi:10.1117/12.3005557

20 September 2023 Prediction uncertainty evaluation of deep learning ghost imaging based on the feature map

S. Kataoka, Y. Mizutani, T. Uenohara, Y. Takaya

Proceedings Volume 12607, Optical Technology and Measurement for Industrial Applications Conference; 126070P (2023) https://doi.org/10.1117/12.3005557
Event: Optics and Photonics International Congress, 2023, Yokohama, Japan

Abstract

Recently, there has been a high demand for high-quality imaging from weak light in measuring micro-defects. Deep Learning Ghost Imaging (DLGI) has been proposed as a fast and sensitive imaging method for defect inspection. However, measurement with deep learning has a problem evaluating the prediction uncertainty. The predicted value from deep learning is distributed close to the true value in the feature, while the traditional measurement value is physically distributed close to the true value. Then, applying the conventional uncertainty evaluation method based on statistics is difficult. To overcome this problem, we propose the evaluation method of the prediction uncertainty based on the feature map in the middle layer of the CNN. By adding random numbers to the middle layer, several close estimates of feature values can be obtained. The standard deviation of these estimates is defined as prediction uncertainty. This paper shows the numerical comparison of the proposed method with evaluation by data augmentation, which evaluates the prediction uncertainty by adding fluctuations to the input data. The data augmentation method can estimate the uncertainty of changes in measurement conditions. Although the data augmentation method does not provide enough change for low SNR data, which makes uncertainty evaluation difficult, the proposed method offers constant fluctuation even for low SNR data. We have numerically confirmed that the proposed method can accurately evaluate the prediction uncertainty even for low SNR.

Citation Download Citation

S. Kataoka, Y. Mizutani, T. Uenohara, and Y. Takaya "Prediction uncertainty evaluation of deep learning ghost imaging based on the feature map", Proc. SPIE 12607, Optical Technology and Measurement for Industrial Applications Conference, 126070P (20 September 2023); https://doi.org/10.1117/12.3005557

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KEYWORDS

Signal to noise ratio

Deep learning

Light sources and illumination

Measurement uncertainty

Defect inspection

Image restoration

Education and training

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