Image disambiguation with deep neural networks

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.