Emergent physics-informed design of deep learning for microscopy. (14th April 2021)

• Record Type: Journal Article
• Authors: Wijesinghe, Philip; Dholakia, Kishan
• Is Part Of: JPhys photonics. Volume 3:Number 2(2021)
• Publication Date: 2021-04-14
• Subjects: deep learning -- microscopy -- inverse methods -- physics-informed learning -- computational imaging; Photonics -- Periodicals; 621.365
• DOI: 10.1088/2515-7647/abf02c
• Languages: English
• ISSNs: 2515-7647

Abstract

Abstract: Deep learning has revolutionised microscopy, enabling automated means for image classification, tracking and transformation. Beyond machine vision, deep learning has recently emerged as a universal and powerful tool to address challenging and previously untractable inverse image recovery problems. In seeking accurate, learned means of inversion, these advances have transformed conventional deep learning methods to those cognisant of the underlying physics of image formation, enabling robust, efficient and accurate recovery even in severely ill-posed conditions. In this perspective, we explore the emergence of physics-informed deep learning that will enable universal and accessible computational microscopy.