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

Gaussian noise removal is an interesting area in digital image processing not only to improve the visual quality, but for its impact on other post-processing algorithms like image registration or segmentation. Many presented state-of-the-art denoising methods are based on the self-similarity or patc...

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Bibliographic Details
Published inarXiv.org
Main Authors Kazemi, Mojtaba, Ehsan, Mohammadi P, Parichehr shahidi sadeghi, Menhaj, Mohamad B
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 03.12.2016
Subjects
Algorithms
Digital imaging
Euclidean geometry
Image filters
Image processing
Image registration
Image segmentation
Kernels
Noise
Noise generation
Noise levels
Noise reduction
Pixels
Post-processing
Random noise
Self-similarity
Weighting
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Summary:Gaussian noise removal is an interesting area in digital image processing not only to improve the visual quality, but for its impact on other post-processing algorithms like image registration or segmentation. Many presented state-of-the-art denoising methods are based on the self-similarity or patch-based image processing. Specifically, Non-Local Means (NLM) as a patch-based filter has gained increasing attention in recent years. Essentially, this filter tends to obtain the noise-less signal value by computing the Gaussian-weighted Euclidean distance between the patch under-processing and other patches inside the image. However, the NLM filter is sensitive to the outliers (pixels that their intensity values are far away from other pixels) inside the patch, meaning that the pixels with the symmetric locations in the patch are assigned the same weight. This can lead to sub-optimal denoising performance when the destructive nature of noise generates some outliers inside patches. In this paper, we propose a new weighting approach to modify the Gaussian kernel of the NLM filter. Our approach employs the geometric distance between image intensities to come up with new weights for each pixel of a patch, lowering the impact of outliers on the denoising performance. Experiments on a set of standard images and different noise levels show that our proposed method outperforms the other compared denoising filters.
ISSN:2331-8422