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Open AccessArticle

Low-Element Image Restoration Based on an Out-of-Order Elimination Algorithm

1
Electronic Engineering College, Heilongjiang University, Harbin 150080, China
2
Communications Research Center, Harbin Institute of Technology, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1192; https://doi.org/10.3390/e21121192
Received: 15 November 2019 / Revised: 3 December 2019 / Accepted: 3 December 2019 / Published: 4 December 2019
To reduce the consumption of receiving devices, a number of devices at the receiving end undergo low-element treatment (the number of devices at the receiving end is less than that at the transmitting ends). The underdetermined blind-source separation system is a classic low-element model at the receiving end. Blind signal extraction in an underdetermined system remains an ill-posed problem, as it is difficult to extract all the source signals. To realize fewer devices at the receiving end without information loss, this paper proposes an image restoration method for underdetermined blind-source separation based on an out-of-order elimination algorithm. Firstly, a chaotic system is used to perform hidden transmission of source signals, where the source signals can hardly be observed and confidentiality is guaranteed. Secondly, empirical mode decomposition is used to decompose and complement the missing observed signals, and the fast independent component analysis (FastICA) algorithm is used to obtain part of the source signals. Finally, all the source signals are successfully separated using the out-of-order elimination algorithm and the FastICA algorithm. The results show that the performance of the underdetermined blind separation algorithm is related to the configuration of the transceiver antenna. When the signal is 3 × 4antenna configuration, the algorithm in this paper is superior to the comparison algorithm in signal recovery, and its separation performance is better for a lower degree of missing array elements. The end result is that the algorithms discussed in this paper can effectively and completely extract all the source signals.
Keywords: underdetermined blind-source separation; chaotic system; empirical mode decomposition; FastICA underdetermined blind-source separation; chaotic system; empirical mode decomposition; FastICA
MDPI and ACS Style

Xie, Y.; Yu, J.; Chen, X.; Ding, Q.; Wang, E. Low-Element Image Restoration Based on an Out-of-Order Elimination Algorithm. Entropy 2019, 21, 1192.

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