Image disambiguation with deep neural networks

Omar DeGuchy; Alex Ho; Roummel F. Marcia

doi:10.1117/12.2530230

6 September 2019 Image disambiguation with deep neural networks

Omar DeGuchy, Alex Ho, Roummel F. Marcia

Proceedings Volume 11139, Applications of Machine Learning; 111390A (2019) https://doi.org/10.1117/12.2530230
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

In many signal recovery applications, measurement data is comprised of multiple signals observed concurrently. For instance, in multiplexed imaging, several scene subimages are sensed simultaneously using a single detector. This technique allows for a wider field-of-view without requiring a larger focal plane array. However, the resulting measurement is a superposition of multiple images that must be separated into distinct components. In this paper, we explore deep neural network architectures for this image disambiguation process. In particular, we investigate how existing training data can be leveraged and improve performance. We demonstrate the effectiveness of our proposed methods on numerical experiments using the MNIST dataset.

Citation Download Citation

Omar DeGuchy, Alex Ho, and Roummel F. Marcia "Image disambiguation with deep neural networks", Proc. SPIE 11139, Applications of Machine Learning, 111390A (6 September 2019); https://doi.org/10.1117/12.2530230

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