非局部意味着使用自适应核去噪

20th Iranian Conference on Electrical Engineering (ICEE2012) Pub Date : 2012-05-15 DOI:10.1109/IRANIANCEE.2012.6292584

A. Tahmouresi, S. Saryazdi, S. Seydnejad

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引用次数: 4

摘要

非局部均值算法是一种功能强大的图像去噪方法。在噪声图像的边缘和纹理部分附近保持噪声是NLM的主要缺点之一。本文引入了一种基于图像结构的自适应核函数，用于去除图像中的残留噪声。实验结果表明，该算法与原始NLM和基于形状自适应补丁的方法相比具有优越性。

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Non-local means denoising using an adaptive kernel

Non-local means algorithm is one of the powerful image denoising methods. Maintaining noise near edges and textural parts of a noisy image, is one of the main drawbacks of NLM. In this paper we introduce an adaptive kernel derived from image structure to remove maintained noise. Experimental results show superiority of our algorithm in comparison with original NLM as well as a method based on shape adaptive patches.

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20th Iranian Conference on Electrical Engineering (ICEE2012)

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