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Gaussian noise removal is an interesting area in digital image processing notonly to improve the visual quality, but for its impact on other post-processingalgorithms like image registration or segmentation. Many presentedstate-of-the-art denoising methods are based on the self-similarity orpatch-based image processing. Specifically, Non-Local Means (NLM) as apatch-based filter has gained increasing attention in recent years.Essentially, this filter tends to obtain the noise-less signal value bycomputing the Gaussian-weighted Euclidean distance between the patchunder-processing and other patches inside the image. However, the NLM filter issensitive to the outliers (pixels that their intensity values are far away fromother pixels) inside the patch, meaning that the pixels with the symmetriclocations in the patch are assigned the same weight. This can lead tosub-optimal denoising performance when the destructive nature of noisegenerates some outliers inside patches. In this paper, we propose a newweighting approach to modify the Gaussian kernel of the NLM filter. Ourapproach employs the geometric distance between image intensities to come upwith new weights for each pixel of a patch, lowering the impact of outliers onthe denoising performance. Experiments on a set of standard images anddifferent noise levels show that our proposed method outperforms the othercompared denoising filters.

A Non-Local Means Approach for Gaussian Noise Removal from Images using a Modified Weighting Kernel