Deep learning for microscopy and measurement

Phillip Wijesinghe; Kishan Dholakia

doi:10.1117/12.2571320

22 August 2020 Deep learning for microscopy and measurement

Phillip Wijesinghe, Kishan Dholakia

Proceedings Volume 11463, Optical Trapping and Optical Micromanipulation XVII; 114631I (2020) https://doi.org/10.1117/12.2571320
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Abstract

Deep learning is emerging as a powerful technique in many areas of sciences, from evolutionary biology to quantum physics. They may use artificial neural networks (ANNs) to automatically learn to identify and extract the relevant features present in an input dataset. In this paper we will describe the use of such approaches in a number of applications. We show how to recover high-resolution images from sub-sampled information in Airy beam light sheet microscopy as well as analyse and understand measurement using laser speckle. Due consideration will be given to the role of noise in datasets, limitations of these approaches and future directions

Conference Presentation

Citation Download Citation

Phillip Wijesinghe and Kishan Dholakia "Deep learning for microscopy and measurement", Proc. SPIE 11463, Optical Trapping and Optical Micromanipulation XVII, 114631I (22 August 2020); https://doi.org/10.1117/12.2571320

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