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Imaging through scattering media using speckle pattern classification based support vector regression
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DOI：10.1364/OE.26.026663.png)
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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. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Keyword:
SPARSE REPRESENTATION
OBJECT RECOGNITION
FOURIER-DOMAIN
FOCUSING LIGHT
ALGORITHM
DICTIONARIES
TOMOGRAPHY
LAYERS
FIELD
TIME
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
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