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Proceedings Paper

Learning speckle correlations for imaging through scattering (Conference Presentation)

Paper 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) frame- work. 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.

Paper Details

Date Published: 13 May 2019
Proc. SPIE 10990, Computational Imaging IV, 1099002 (13 May 2019); doi: 10.1117/12.2520081
Show Author Affiliations
Yunzhe Li, Boston Univ. (United States)
Yujia Xue, Boston Univ. (United States)
Lei Tian, Boston Univ. (United States)

Published in SPIE Proceedings Vol. 10990:
Computational Imaging IV
Abhijit Mahalanobis; Lei Tian; Jonathan C. Petruccelli, Editor(s)

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