Imaging through turbid media via sparse representation: imaging quality comparison of three projection matrices

Xiaopeng Shao; Huijuan Li; Tengfei Wu; Weijia Dai; Xiangli Bi

doi:10.1117/12.2176733

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13 May 2015 Imaging through turbid media via sparse representation: imaging quality comparison of three projection matrices

Xiaopeng Shao, Huijuan Li, Tengfei Wu, Weijia Dai, Xiangli Bi

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Proceedings Volume 9487, Smart Biomedical and Physiological Sensor Technology XII; 948711 (2015) https://doi.org/10.1117/12.2176733
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, Maryland, United States

Abstract

The incident light will be scattered away due to the inhomogeneity of the refractive index in many materials which will greatly reduce the imaging depth and degrade the imaging quality. Many exciting methods have been presented in recent years for solving this problem and realizing imaging through a highly scattering medium, such as the wavefront modulation technique and reconstruction technique. The imaging method based on compressed sensing (CS) theory can decrease the computational complexity because it doesn't require the whole speckle pattern to realize reconstruction. One of the key premises of this method is that the object is sparse or can be sparse representation. However, choosing a proper projection matrix is very important to the imaging quality. In this paper, we analyzed that the transmission matrix (TM) of a scattering medium obeys circular Gaussian distribution, which makes it possible that a scattering medium can be used as the measurement matrix in the CS theory. In order to verify the performance of this method, a whole optical system is simulated. Various projection matrices are introduced to make the object sparse, including the fast Fourier transform (FFT) basis, the discrete cosine transform (DCT) basis and the discrete wavelet transform (DWT) basis, the imaging performances of each of which are compared comprehensively. Simulation results show that for most targets, applying the discrete wavelet transform basis will obtain an image in good quality. This work can be applied to biomedical imaging and used to develop real-time imaging through highly scattering media.

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Xiaopeng Shao, Huijuan Li, Tengfei Wu, Weijia Dai, and Xiangli Bi "Imaging through turbid media via sparse representation: imaging quality comparison of three projection matrices", Proc. SPIE 9487, Smart Biomedical and Physiological Sensor Technology XII, 948711 (13 May 2015); https://doi.org/10.1117/12.2176733

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