Image Denoising using Stochastic Resonance

This paper presents an algorithm for noise removal from digital image, based on stochastic noise pattern. We apply white Gaussian noise to improve the quality of the noisy input image to get the de -noised response image. Input noisy image is subjected to independent additive white Gaussian noise of different standard deviation, the output image corresponding to individual noise standard deviation, summed and averaged, to get the denoised image. This behavior is termed as “Suprathreshold Stochastic Resonance” (SSR) [1]. We have shown that SSR occurs for image de-noising. Here, threshold is taken as the mean of the noise added input noisy image. Generally, threshold phenomenon plays a major role in stochastic resonance (SR) and supra-threshold stochastic resonance (SSR). Depending on the threshold value, non-dynamical SR or SSR condition can be set up. The results of these are quantified appropriately through visualization of an output image and through the plot of PSNR. Key issue of our work is the reconstruction of the input noisy image by stochastic noise pattern that reflect better features of the image. Block diagram of our algorithm for SSR and method for finding threshold equation to be used in image denoising are also presented in this paper.