Algorithm of image de-noising for Gaussian-Gaussian mixed noise model based on stochastic resonance

Jianxin Chen; Yu Zhang; Jiazhong Song

doi:10.1117/12.2539625

14 August 2019 Algorithm of image de-noising for Gaussian-Gaussian mixed noise model based on stochastic resonance

Jianxin Chen, Yu Zhang, Jiazhong Song

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 1117904 (2019) https://doi.org/10.1117/12.2539625
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

It is of great significance to image de-noising since the image has been the main medium of acquiring and transmitting information in human life. The image is not only destroyed by signal-independent noise, but also destroyed by signaldependent noise largely. In order to fill the gap between stochastic resonance for image processing with signalindependent noise and signal-dependent noise and eliminate the shortcoming of unsatisfactory processing results of image in the traditional image de-noising methods, an algorithm of image de-noising for Gaussian-Gaussian mixed noise based on stochastic resonance is innovatively proposed in this paper. And three main steps involved are image segmentation, add mixed noise to the image and stochastic resonance processing. First, the original image is clustered and segmented to obtain multiple regions. Then, signal-independent Gaussian noise and signal-dependent Gaussian noise are added to the image in sequence. Finally the multiple noisy regions are respectively processing by stochastic resonance. The proposed method is experimented with different noise variance combinations. The experimental results show that the proposed method can achieve higher peak-signal-to-noise (PSNR) and structural similarity (SSIM), and meanwhile the visual effect is also better.

Citation Download Citation

Jianxin Chen, Yu Zhang, and Jiazhong Song "Algorithm of image de-noising for Gaussian-Gaussian mixed noise model based on stochastic resonance", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117904 (14 August 2019); https://doi.org/10.1117/12.2539625

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