Open Access Paper
17 April 2019 Deep learning assisted optical wavefront shaping in disordered medium (Erratum)
Abstract
A revised version of this paper 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.

A citation to ‘Light scattering control in transmission and reflection with neural networks’ (Optics Express Vol. 26, Issue 23, pp. 30911-30929, 2018), as reference [11], has been added in Section 2, paragraph 4. Figure 1 and its caption have been modified as follows:

Figure 1.

Illustration of wavefront shaping using neural networks. Light undergoes multiple scattering inside inhomogeneous media, and incident light with different SLM patterns results in different output speckle patterns. The speckle patterns serve as inputs to a pre-constructed convolutional neural network while the SLM patterns as the outputs. After proper training, convolutional neural networks are able to establish the relationship between the speckle patterns to their corresponding SLM patterns.

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The authors would like to apologize for the inconvenience caused for readers and state that these do not change the scientific conclusions of the article in any way. The original article has been correspondingly updated.

© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
"Deep learning assisted optical wavefront shaping in disordered medium (Erratum)", Proc. SPIE 10886, Adaptive Optics and Wavefront Control for Biological Systems V, 108861J (17 April 2019); https://doi.org/10.1117/12.2535809
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KEYWORDS
Speckle pattern

Wavefronts

Spatial light modulators

Convolutional neural networks

Light scattering

Neural networks

Cameras

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