Exploit imaging through opaque wall via deep learning

Abstract

Imaging through scattering media is encountered in many disciplines or sciences, ranging from biology, mesescopic physics and astronomy. But it is still a big challenge because light suffers from multiple scattering is such media and can be totally decorrelated. Here, we propose a deep-learning-based method that can retrieve the image of a target behind a thick scattering medium. The method uses a trained deep neural network to fit the way of mapping of objects at one side of a thick scattering medium to the corresponding speckle patterns observed at the other side. For demonstration, we retrieve the images of a set of objects hidden behind a 3mm thick white polystyrene slab, the optical depth of which is 13.4 times of the scattering mean free path. Our work opens up a new way to tackle the longstanding challenge by using the technique of deep learning.

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Cited by in corpus (5)

• High-Resolution Limited-Angle Phase Tomography of Dense Layered Objects Using Deep Neural Networks
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• Direct Object Recognition Without Line-of-Sight Using Optical Coherence
• Transfer Learning in General Lensless Imaging through Scattering Media
• Imaging-free object recognition enabled by optical coherence