A one-for-all deep learning approach for imaging through diffusers (Conference Presentation)

Yunzhe Li; Yujia Xue; Lei Tian

doi:10.1117/12.2507276

4 March 2019 A one-for-all deep learning approach for imaging through diffusers (Conference Presentation)

Yunzhe Li, Yujia Xue, Lei Tian

Author Affiliations +

Proceedings Volume 10886, Adaptive Optics and Wavefront Control for Biological Systems V; 108860X (2019) https://doi.org/10.1117/12.2507276
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

Light scattering in complex media is a pervasive problem across many areas, such as deep tissue imaging, and imaging in degraded environment. Major progress has been made by using the transmission matrix (TM) framework that characterizes the “one-for-one” input-output relation of a fixed scattering medium as a linear shift-variant matrix. A major limitation of these existing approaches is their high susceptibility to model errors. The phase-sensitive TM is inherently intolerant to speckle decorrelations. Our goal here is to develop a highly scalable imaging through scattering framework by overcoming the existing limitations in susceptibility to speckle decorrelation and SBP. The proposed model is built on a deep learning (DL) framework. To satisfy the desired statistical properties, we do not train a convolutional neural network (CNN) to learn the TM of a single scattering medium. Instead, we build a CNN to learn a “one-for-all” mapping by training on several scattering media with different microstructures while having the same macroscopic parameter. Specifically, we show that our CNN model trained on a few diffusers can sufficiently support the statistical information of all diffusers having the same mean characteristics (e.g. “grits”). We then experimentally demonstrate that the CNN is able to “invert” speckles captured from entirely different diffusers to make high-quality object predictions. Our method significantly improves the system’s information throughput and adaptability as compared to existing approaches, by improving both the SBP and the robustness to speckle decorrelations.

Conference Presentation

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Yunzhe Li, Yujia Xue, and Lei Tian "A one-for-all deep learning approach for imaging through diffusers (Conference Presentation)", Proc. SPIE 10886, Adaptive Optics and Wavefront Control for Biological Systems V, 108860X (4 March 2019); https://doi.org/10.1117/12.2507276

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KEYWORDS

Diffusers

Scattering

Speckle

Light scattering

Associative arrays

Convolutional neural networks

Deep tissue imaging

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