Information retrieval from optical speckles is desired yet challenging. Insufficient sampling, especially in sub-Nyquist domain, of speckles significantly destroys the encoded information and correlations among these speckle grains. To address that, we trained a deep neural network to combat the physical imperfection: the sub-Nyquist sampled speckles (~14 below the Nyquist criterion) are interpolated up to a well-resolved level (322 times more pixels to resolve the same FOV) with smoothed morphology fine-textured, and more importantly, lost information retraced. With the FOV-resolution dilemma favorably overcome, it deepens our understanding of the scattering, enabling big and clear imaging in complex scenarios.
Yunqi Luo, Huanhao Li, Ruochong Zhang, Puxiang Lai, and Yuanjin Zheng "Deep learning assisted optical wavefront shaping in disordered medium", Proc. SPIE 10886, Adaptive Optics and Wavefront Control for Biological Systems V, 1088612 (20 February 2019); doi: 10.1117/12.2504425
was published on 17 April 2019.
Details of the revision are provided in the text that accompanies this Erratum. The original paper has been updated.
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