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Abstract

Non-local means algorithm can remove image noise in a unique way that is contrary to traditional techniques. This is because it not only smooths the image but it also preserves the information details of the image. However, this method suffers from high computational complexity. We propose a multi-scale non-local means method in which adaptive multi-scale technique is implemented. In practice, based on each selected scale, the input image is divided into small blocks. Then, we remove the noise in the given pixel by using only one block. This can overcome the low efficiency problem caused by the original non-local means method. Our proposed method also benefits from the local average gradient orientation. In order to perform evaluation, we compared the processed images based on our technique with the ones by the original and the improved non-local means denoising method. Extensive experiments are conducted and results shows that our method is faster than the original and the improved non-local means method. It is also proven that our implemented method is robust enough to remove noise in the application of neuroimaging.

Keywords

Non-local means denoising adaptive multi-scale average gradient orientation neuroimaging

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Multi-scale non-local denoising method in neuroimaging

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