Journal
PHYSICS LETTERS A
Volume 383, Issue 13, Pages 1457-1465Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.physleta.2019.02.006
Keywords
Adaptive array stochastic resonance; Hilbert scanning; Low PSNR; Classical image filtering restoration methods
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Funding
- National Natural Science Foundation of China [61179027]
- Natural Science Foundation of the Higher Education Institutions of Jiangsu Province of China [18KJB520035]
- Open Foundation of National Engineering Research Center of Communications and Networking [GCZX001]
- Youth Foundation of Nanjing University of Finance and Economics [L-JXL18002]
- Youth Foundation of Nanjing University of Posts and Telecommunications [NY218142]
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Considering the widespread noise interference in the two-dimensional (2D) image transmission processing, we proposed an optimal adaptive bistable array stochastic resonance (SR)-based grayscale image restoration enhancement method under low peak signal-to-noise ratio (PSNR) environments. In this method, the Hilbert scanning is adopted to reduce the dimension of the original grayscale image. The 2D image signal is converted into a one-dimensional (1D) binary pulse amplitude modulation (BPAM) signal. Meanwhile, we use the adaptive bistable array SR module to enhance the 1D low SNR BPAM signal. In order to obtain the restored image, we transform the enhanced BPAM signal into a 2D grayscale image signal. Simulation results show that the proposed method significantly outperforms the classical image restoration methods (i.e., mean filter, Wiener filter and median filter) both on the grayscale level and the PSNR of the restored image, particularly in a low PSNR scenario. Larger array size brings better image restoration effect. (C) 2019 Elsevier B.V. All rights reserved.
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