Fast terahertz reflection tomography using block-based compressed sensing
ABSTRACT In this paper, a new fast terahertz reflection tomography is proposed using block-based compressed sensing. Since measuring the time-domain signal on two-dimensional grid requires excessive time, reducing measurement time is highly demanding in terahertz tomography. The proposed technique directly reduces the number of sampling points in the spatial domain without modulation or transformation of the signal. Compressed sensing in spatial domain suggests a block-based reconstruction, which substantially reduces computational time without degrading the image quality. An overlap-average method is proposed to remove the block artifact in the block-based compressed sensing. Fast terahertz reflection tomography using the block-based compressed sensing is demonstrated with an integrated circuit and parched anchovy as examples.
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ABSTRACT: Terahertz (THz) imaging is a nondestructive, label-free, rapid imaging technique which gives the possibility of a real-time tracing of drugs within the skin. We evaluated the feasibility of THz dynamic imaging for visualizing serial changes in the distribution and penetration of a topical agent, dimethyl sulfoxide (DMSO) containing ketoprofen, using excised mouse skins. THz imaging was performed for 6 h after drug application to the skin and was compared with the results obtained using the Franz cell diffusion test, a standard in vitro skin absorption test. THz dynamic reflection imaging showed that the reflection signals decreased rapidly during the early time period, and remained constant through the late time period. The area of drug permeation within the skin layer on THz imaging increased with time. The dynamic pattern of THz reflection signal decrease was similar to that of DMSO absorption analyzed by the Franz cell diffusion test, which indicates that THz imaging mainly reflects the DMSO component. This study demonstrates that THz imaging technique can be used for imaging the spatial distribution and penetration of drug-applied sites.Optics Express 04/2012; 20(9):9476-84. DOI:10.1364/OE.20.009476 · 3.53 Impact Factor
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ABSTRACT: A block-based compressive imaging (BCI) system using sequential architecture is presented in this paper. Feature measurements are collected using the principal component analysis (PCA) projection. The linear Wiener operator and a nonlinear method based on the Field-of-Expert (FoE) prior model are used for object reconstruction. Experimental results are given to demonstrate the superior reconstruction performance of the FoE-based method over the Wiener operator. In addition, the effects of system parameters, such as the object block size, the number of features per block, and the noise level to the BCI reconstruction performance are discussed with different kinds of objects. Then an optimal block size is defined and studied for BCI.Optics Express 09/2012; 20(20):22102-17. DOI:10.1364/OE.20.022102 · 3.53 Impact Factor
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ABSTRACT: When an image is reconstructed by the conventional compressed sensing with random measurement points, most degradation in the reconstructed image occurs in the transient regions. To solve this problem, in this paper, an adaptive compressed sensing that estimates the transient regions in the image and acquires more data at those regions is proposed, which can reconstruct an image with higher quality. The proposed method roughly analyzes the characteristics of image using the randomly-acquired data, acquires additional data at the adaptively-determined points based on the image characteristics, and reconstructs the final image. It is confirmed that with the same number of acquired data, the proposed method reconstructs the image of higher quality than the conventional method.09/2012; 49(9). DOI:10.5573/ieek.2012.49.9.085