Deep learning reconstructs holograms

Deep learning has been experiencing a true renaissance especially over the last decade, and it uses multi-layered artificial neural networks for automated analysis of data. Deep learning is one of the most exciting forms of machine learning that is behind several recent leapfrog advances in technology including for example real-time speech recognition and translation as well image/video labeling and captioning, among many others. Especially in image analysis, deep learning shows significant promise for automated search and labeling of features of interest, such as abnormal regions in a medical image. Now, UCLA researchers have demonstrated a new use for deep learning – this time to reconstruct a hologram and form a microscopic image of an object. In a recent article that is published in Light: Science & Applications, a journal of the Springer Nature, UCLA researchers have demonstrated that a neural network can learn to perform phase recovery and holographic image reconstruction after appropriate training.