Spatially Localized Implementation of SSR and DSR for Image Denoising

Abstract

This paper presents an algorithm for noise removal from digital image, based on stochastic noise pattern. We propose spatially localized stochastic resonance for image denoising implemented using Suprathreshold Stochastic Resonance (SSR) and Dynamic Stochastic Resonance (DSR). The approach has been tested on 300 images. For SSR implementation, 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. For DSR implementation, singular values are used for noise suppression in digital images. Key issue of our work is the reconstruction of the input noisy image by stochastic noise pattern using SSR and DSR that reflect better features of the image. The results of these are quantified appropriately through PSNR (peak signal-to-noise ratio) and visualization of an output image. The localized implementation was tested on a dataset of 300 images and when compared with the conventional techniques, our approach is found to give marginally better noise reduction in most of the cases. The average PSNR improvement is considerably improved.