Noise-aided edge-preserving image denoising using non-local means with stochastic resonance

Noise-aided stochastic resonance has been explored in recent literature as a powerful tool that enhances the performance of non-linear systems, particularly in image enhancement and image watermarking. In this paper, we extend the application of stochastic resonance to improve the performance of the conventional non-local means (NLM) filtering for edge-preserving image denoising. The NLM algorithm typically involves computation of weights denoting similarity of a pixel with all other pixels in the image. In the proposed algorithm, these similarity weights are iteratively processed using the concept of dynamic stochastic resonance. The results indicate a significant improvement in sharpness of edges in the denoised images in comparison with the conventional NLM approach both visually and quantitatively in terms of full-reference and no-reference image quality metrics.