Extension of Non-Local Means (NLM) algorithm with Gaussian filtering for highly noisy images

摘要

The denoising performance of the Non-Local Means (NLM) method decreases as the variance of additive white Gaussian noise becomes higher. In this paper, we explain this phenomenon and propose a modified version of the Non-Local Means (NLM) method, called the Enhanced-Weights NLM (EWNLM) algorithm, to denoise highly noisy images. The EWNLM algorithm evaluates weights from a pre-filtered image using the Gaussian kernel, which in turn result in more robust weight contributions from similar pixels in the search window. Experimental results are given to demonstrate the superior performance of the EWNLM scheme when the standard deviation of the additive white Gaussian noise (AWGN) is greater than 20.