Terahertz compressive holographic tomography has been one of the most investigated research topics ever since the generation of terahertz signals. The accuracy of the 3D reconstruction results are influenced by samples size directly. This paper mainly studies that different samples size influence the compressive sensing algorithm iteration number and sparse restriction parameters on the 2.52THz reconstruction results under Gaussian noise conditions. The best reconstruction parameters are given and compared with condition of no noise. This simulation study is possible benefit to the practical application.
KEYWORDS: Holograms, Holography, Tomography, Reconstruction algorithms, Optical simulations, Compressed sensing, Image quality, 3D image reconstruction, Digital holography, Signal to noise ratio
The terahertz Gabor inline compressive holographic tomography can obtain richer information than 2D images and has potential for practical application. Due to the current terahertz detector sensitivity is not high enough and the laser light intensity is weak, we often make some holograms added together and then averaging. At the same time, the holograms averaging can eliminate speckle noise in holography. Considering the actual target and its back-bottom material are inhomogeneous, we simulated the target and its background that containing Gaussian noise. This paper mainly studies that the holograms averaging influence the compressive sensing algorithm sparse restriction parameters on the reconstruction results under Gaussian noise conditions when iteration number is 200. Finally, we give the best reconstruction parameters. This simulation study is possible benefit to the practical application.
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