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arXiv · 1806.08015

Stability of Scattering Decoder For Nonlinear Diffractive Imaging

Yu Sun·Ulugbek S. Kamilov
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Abstract

The problem of image reconstruction under multiple light scattering is usually formulated as a regularized non-convex optimization. A deep learning architecture, Scattering Decoder (ScaDec), was recently proposed to solve this problem in a purely data-driven fashion. The proposed method was shown to substantially outperform optimization-based baselines and achieve state-of-the-art results. In this paper, we thoroughly test the robustness of ScaDec to different permittivity contrasts, number of transmissions, and input signal-to-noise ratios. The results on high-fidelity simulated datasets show that the performance of ScaDec is stable in different settings.

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Yu Sun, Ulugbek S. Kamilov. 2018-06-20. Stability of Scattering Decoder For Nonlinear Diffractive Imaging. https://arxiv.org/abs/1806.08015

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