https://doi.org/10.1140/epjp/s13360-021-01693-5
Regular Article
Adaptive denoising for strong noisy images by using positive effects of noise
1
Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, School of Mechatronic Engineering, China University of Mining and Technology, 221116, Xuzhou, Jiangsu, People’s Republic of China
2
Nonlinear Dynamics, Chaos and Complex Systems Group, Departamento de Física, Universidad Rey Juan Carlos, Tulipán s/n, 28933, Móstoles, Madrid, Spain
3
Department of Applied Informatics, Kaunas University of Technology, Studentu 50-407, 51368, Kaunas, Lithuania
4
School of Mechanical and Electronic Engineering, Lanzhou University of Technology, 730050, Lanzhou, People’s Republic of China
Received:
31
March
2021
Accepted:
22
June
2021
Published online:
28
June
2021
Image denoising is the key step for image preprocessing. In particular, denoising of a strong noisy image is truly necessary, even though it might be difficult. Among the different image denoising methods, stochastic resonance (SR) has the advantage of using the constructive role of noise. However, a traditional bistable system cannot take full advantage of SR. To improve the performance of image denoising by using SR, we use a nonlinear system with a periodic potential to utilize the benefit of noise to a greater extent. Besides, adaptive processing is realized by an optimization algorithm. Compared to the image denoising method by SR in a bistable system, the method by SR in a nonlinear system with a periodic potential (PSR) is much more effective possessing a higher peak signal-to-noise ratio and less computation time. Further, the image denoising method by PSR is also superior to other common methods such as arithmetic mean filter, geometric mean filter, median filter and Wiener filter. The PSR method is effective in removing different types of noise present in images, such as gamma noise, uniform noise, Rayleigh noise, and exponential noise.
© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2021
