Image denoising using adaptive bi-dimensional stochastic resonance system

Abstract

AbstractUsing stochastic resonance (SR) mechanism, the output signal can be enhanced by adding noise to the nonlinear system. Therefore, an image denoising algorithm based on adaptive bi-dimensional stochastic resonance (ABSR) is proposed in this paper. Firstly, the image is sampled as a bi-dimensional signal, and an adaptive bi-dimensional dynamic nonlinear system model is constructed. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the output image are used as the double evaluation model of the adaptive system, and the optimal parameters of the model are automatically obtained by adjusting the parameters of the dynamic nonlinear system using the reverse positioning method. Compared with the traditional mean filter, median filter and one-dimensional stochastic resonance, the image restoration effect of dynamic adaptive bi-dimensional stochastic resonance is more closer to the original image, and the histogram, PSNR and SSIM of the output image are also significantly better than the other three methods. The results show that dynamic adaptive bi-dimensional stochastic resonance has better denoising effect and better robustness to the change of noise intensity in image processing.Keywords: Image denoisingstochastic resonancebi-dimensional system AcknowledgementsThe authors would like to thank foreign friends for proofreading the manuscript. The authors are also grateful to the anonymous reviewers for their valuable comments and suggestions.Additional informationFundingThis research was supported by [National Natural Science Foundation of China #1] under Grant [number 11672207]; [Tianjin Natural Science Foundation of China] under Grant [number 17JCYBJC15700]; and [research and application of key technologies of intelligent robot process automation] under Grant [number 1500/2022-72002B].