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Open AccessJournal ArticleDOI

Imaging through scattering media using speckle pattern classification based support vector regression.

Hui Chen, +3 more
- 01 Oct 2018 - 
- Vol. 26, Iss: 20, pp 26663-26678
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TLDR
Experimental results show that, with the proposed approach, speckle patterns could be utilized for classification when object images are unavailable, and object images can be reconstructed with high fidelity.
Abstract
Imaging through scattering media is a common practice in many applications of biomedical imaging. Object image would deteriorate into unrecognizable speckle pattern when scattering media is presented. Many methods have been investigated to reconstruct the object image when only speckle pattern is available. In this paper, we demonstrate a method of single-shot imaging through scattering media. This method is based on classification and support vector regression of the measured speckle pattern. We prove the possibility of speckle pattern classification and related formulas are presented. The specified and limited imaging capability without speckle pattern classification is demonstrated. Our proposed approach, that is, speckle pattern classification based support vector regression method, makes up the deficiency. Experimental results show that, with our approach, speckle patterns could be utilized for classification when object images are unavailable, and object images can be reconstructed with high fidelity. The proposed approach for imaging through scattering media is expected to be applicable to various sensing schemes.

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Citations
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Optronic convolutional neural networks of multi-layers with different functions executed in optics for image classification.

TL;DR: In this paper, an optronic convolutional neural network (OPCNN) is proposed, in which all computation operations are executed in optics, and data transmission and control is executed in electronics.
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Imaging Through Turbid Media With Vague Concentrations Based on Cosine Similarity and Convolutional Neural Network

TL;DR: The combined model presented in this paper is tolerant to the uncertainty of turbidity, it guarantees high-accuracy pattern classification and high-quality image reconstruction, and is feasible for potential applications in harsh water solutions with unknown perturbations of concentrations.
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Research on image transmission mechanism through a multimode fiber based on principal component analysis

TL;DR: A method is proposed that constructs the inverse transformation matrix of the MMF based on principal component analysis (PCA), which can reconstruct the grayscale images of natural scene at high frame rate and high resolution and shows high reconstruction accuracy with few training samples.
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Imaging through scattering media based on semi-supervised learning.

TL;DR: An image-to-image translation, which is called a cycle generative adversarial network (CycleGAN), based on semi-supervised learning with an unlabeled dataset, is used for less-invasive imaging through scattering media.
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High definition images transmission through single multimode fiber using deep learning and simulation speckles

TL;DR: This paper combines principal component analysis (PCA) method, deep learning based speckle classification (DLSC) anddeep learning based image enhancement (DLIE) to improve imaging definition and shows imaging capability with high definition for complex natural scenes, which may provide a feasible method for high definition images transmission through the MMF.
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