2D and 3D computational optical imaging using deep convolutional neural networks (DCNNs)

Thanh Nguyen; George Nehmetallah

doi:10.1117/12.2303995

14 May 2018 2D and 3D computational optical imaging using deep convolutional neural networks (DCNNs)

Thanh Nguyen, George Nehmetallah

Proceedings Volume 10667, Dimensional Optical Metrology and Inspection for Practical Applications VII; 1066702 (2018) https://doi.org/10.1117/12.2303995
Event: SPIE Commercial + Scientific Sensing and Imaging, 2018, Orlando, FL, United States

Abstract

We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D imaging through scattering media. The inverse scattering problem is solved based on learning a huge number of training target and speckle pairs. The proposed technique does not rely on a reference beam, thus employs a simpler optical setup than previous techniques without the need to know the imaging model and optical processes. This lack of the need to know a prior model of the forward operator is very important since many optimization techniques are very sensitive to errors caused by the inaccuracy of the forward model.

Conference Presentation

Citation Download Citation

Thanh Nguyen and George Nehmetallah "2D and 3D computational optical imaging using deep convolutional neural networks (DCNNs)", Proc. SPIE 10667, Dimensional Optical Metrology and Inspection for Practical Applications VII, 1066702 (14 May 2018); https://doi.org/10.1117/12.2303995

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