Image Separation Using Transformer Attention Models

Signal recovery often involves separating and realizing multiple superimposed signals at once. Separating multiple images that have been superimposed is a challenging signal recovery problem. This situation arises when a detector, such as a microphone, receives multiple signals simultaneously. In order to recover the original signals, a signal separator needs to be applied. In this paper, we will explore machine learning techniques for separating such signals. In particular, we investigate two approaches: an autoencoder approach and a transformer-based approach, and test their accuracy in recovering two separate images from noisy low-resolution superimposed measurements.