Medical images often consist of low-contrast objects corrupted by random noise arising in the image acquisition process. Thus,\r\nimage denoising is one of the fundamental tasks required by medical imaging analysis. Nonlocal means (NL-means) method\r\nprovides a powerful framework for denoising. In this work, we investigate an adaptive denoising scheme based on the patch NLmeans\r\nalgorithm for medical imaging denoising. In contrast with the traditional NL-means algorithm, the proposed adaptive\r\nNL-means denoising scheme has three unique features. First, we use a restricted local neighbourhood where the true intensity\r\nfor each noisy pixel is estimated from a set of selected neighbouring pixels to perform the denoising process. Second, the weights\r\nused are calculated thanks to the similarity between the patch to denoise and the other patches candidates. Finally, we apply the\r\nsteering kernel to preserve the details of the images. The proposed method has been compared with similar state-of-art methods\r\nover synthetic and real clinical medical images showing an improved performance in all cases analyzed.
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