Image denoising using dynamic stochastic resonance in wavelet domain

A dynamic stochastic resonance (DSR)-based technique in discrete wavelet transform (DWT) domain for noise suppression in digital images has been proposed in this paper. The initial results on investigation of this concept for denoising of images corrupted by gaussian noise have been presented. Though traditionally noise is considered as undesirable, it has been utilized in the proposed technique to reduce its own effect. In the iterative DSR step, an input noisy image is subjected to independent noise of different standard deviations that iteratively tunes the detail wavelet coefficients, such that the overall effect is the suppression of the degradation due to its own noise. The results are quantified in terms of Noise Mean Value (NMV), Noise Standard Deviation (NSD), and Mean Square Difference (MSD). When compared with the conventional techniques for gaussian denoising, such as gaussian low pass filtering, and soft thresholding of wavelet coefficients, the DSR-based technique is found to give marginally better noise reduction in most of the cases.