JACIII Vol.22 No.1 pp. 70-75
doi: 10.20965/jaciii.2018.p0070


Compressive Sensing of Noisy 3-D Images Based on Threshold Selection

Qingzhu Wang*, Mengying Wei*, and Yihai Zhu**

*School of Information Engineering, Northeast Electric Power University
169 Changchun Road, Jilin City, Jilin 130062, China

**Engineering Technology Center, CRRC Changchun Railway Vehicles Co., Ltd.
435 Qingyin Road, Changchun City, Jilin 130062, China

July 26, 2016
October 11, 2017
January 20, 2018
CS, tensor decomposition, HOSVD, threshold, multidimensional signal processing

Compressive sensing (CS) of high-order data such as hyperspectral images, medical imaging, video sequences, and multi-sensor networks is certainly a hot issue after the emergence of tensor decomposition. Actually, the reconstruction accuracy with current algorithms is not ideal in some cases of noise. In this paper, we propose a new method that can recover noisy 3-D images from a reduced set of compressive measurements. First, multi-way compressive measurements are performed using Gaussian random matrices. Second, the mapping relationship between the variance of noise and the reconstruction threshold is found. Finally, the original images are recovered through reconstruction of pseudo inverse based on threshold selection. We experimentally demonstrate that the proposed method outperforms other similar methods in both reconstruction accuracy (within a range of the compression ratios and different variances of noise) and processing speed.

Cite this article as:
Q. Wang, M. Wei, and Y. Zhu, “Compressive Sensing of Noisy 3-D Images Based on Threshold Selection,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.1, pp. 70-75, 2018.
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