Optimum adaptive array stochastic resonance in noisy grayscale image restoration |
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| Authors: | Jian Liu Bing Hu Youguo Wang |
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| Institution: | 1. College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China;2. College of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;3. College of Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;4. Jiangsu Innovative Coordination Center of Internet of Things, Nanjing 210003, China |
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| Abstract: | 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. |
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| Keywords: | Adaptive array stochastic resonance Hilbert scanning Low PSNR Classical image filtering restoration methods |
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