Lensless computational imaging through deep learning

Abstract

Deep learning has been proven to yield reliably generalizable answers to numerous classification and decision tasks. Here, we demonstrate for the first time, to our knowledge, that deep neural networks (DNNs) can be trained to solve inverse problems in computational imaging. We experimentally demonstrate a lens-less imaging system where a DNN was trained to recover a phase object given a raw intensity image recorded some distance away.

8 pages, 13 figures

References in corpus (1)

• Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal

Cited by in corpus (2)

• Toward a Thinking Microscope: Deep Learning in Optical Microscopy and Image Reconstruction
• Deep Compressive Macroscopic Fluorescence Lifetime Imaging