Robust non-local median filter
Robust non-local median filter
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DOI：
10.1007/s10043-016-0299-2
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发表时间：
2017-01
期刊：
影响因子：
1.2
通讯作者：
Jyohei Matsuoka;Takanori Koga;N. Suetake;E. Uchino
中科院分区：
文献类型：
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作者：
Jyohei Matsuoka;Takanori Koga;N. Suetake;E. Uchino
提出了一种新的图像滤波方法，该方法对叠加在自然灰度图像上的随机值脉冲噪声具有良好的细节保持性能。非局部均值滤波作为一种去除高斯噪声的方法，以其优越的细节保持性能而备受关注。通过借鉴非局部均值的基本概念，我们提出了非局部中值滤波作为去除随机值脉冲噪声的一种特殊方法。在非局部处理中，根据与以感兴趣像素为中心的块相似的块中的像素来计算过滤器的输出。结果，在不破坏原始图像中所有细节结构的情况下，进行了积极的噪声去除。然而，在噪声发生概率较高的情况下，非局部处理的性能会大大降低。这个问题的一个原因是叠加的噪声干扰了块之间相似度的准确计算。针对这一问题，我们提出了一种改进的非局部中值滤波，通过引入一种新的相似性度量来考虑作为原始信号的可能性，从而对严重的腐败程度具有较强的鲁棒性。利用自然灰度图像进行了一系列实验，验证了该方法的有效性和有效性。
This paper describes a novel image filter with superior performance on detail-preserving removal of random-valued impulse noise superimposed on natural gray-scale images. The non-local means filter is in the limelight as a way of Gaussian noise removal with superior performance on detail preservation. By referring the fundamental concept of the non-local means, we had proposed a non-local median filter as a specialized way for random-valued impulse noise removal so far. In the non-local processing, the output of a filter is calculated from pixels in blocks which are similar to the block centered at a pixel of interest. As a result, aggressive noise removal is conducted without destroying the detailed structures in an original image. However, the performance of non-local processing decreases enormously in the case of high noise occurrence probability. A cause of this problem is that the superimposed noise disturbs accurate calculation of the similarity between the blocks. To cope with this problem, we propose an improved non-local median filter which is robust to the high level of corruption by introducing a new similarity measure considering possibility of being the original signal. The effectiveness and validity of the proposed method are verified in a series of experiments using natural gray-scale images.