Improving Nonlocal Means Method via a No-reference Image Content Metric for MRI Denoising

HOU Xin; LI Jianwu; LU Yao; DONG Zhengchao

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Article Navigation > Chinese Journal of Electronics > 2014 > 23(4): 735-741

HOU Xin, LI Jianwu, LU Yao, et al., “Improving Nonlocal Means Method via a No-reference Image Content Metric for MRI Denoising,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 735-741, 2014,

Citation:

HOU Xin, LI Jianwu, LU Yao, et al., “Improving Nonlocal Means Method via a No-reference Image Content Metric for MRI Denoising,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 735-741, 2014,

HOU Xin, LI Jianwu, LU Yao, et al., “Improving Nonlocal Means Method via a No-reference Image Content Metric for MRI Denoising,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 735-741, 2014,

Citation:

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Improving Nonlocal Means Method via a No-reference Image Content Metric for MRI Denoising

1.
Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China;
2.
Brain Imaging Lab, Columbia University, New York, NY 10032, United States;
3.
New York State Psychiatric Institute, New York, NY 10032, United States

Funds: This work is supported by the National Natural Science Foundation of China (No.61271374, No.61273273) and the Beijing Natural Science Foundation (No.4122068).

Received Date: 2014-09-01
Rev Recd Date: 2014-06-01
Publish Date: 2014-10-05

Abstract

Abstract

The patch matching of the traditional Nonlocal means (NLM) filter mainly depends on structure similarity and cannot adapt to the patch rotation or mirroring transformation. Therefore, designing a measure with rotationally invariant similarity is of significant importance for improving the effectiveness of patch comparison of NLM. This paper proposes to apply a no-reference image content metric with the rotation-invariance to NLM for denoising Magnetic resonance (MR) images. The metric measures quantitatively the content of a patch in an image, including sharpness, contrast, and geometric features such as textures and edges. The metric values for every patch are computed and added into the Gaussian matching kernel of NLM so as to effectively perform patch matching. The main advantage of the proposed method is that it does not need to rotate patches in different orientations during patch matching. Experimental results show that the proposed method is superior to the traditional NLM, the state-of-the-art method Block-matching and 3-D (BM3D) filtering and the Unbiased NLM (UNLM) for MRI denoising.
- Nonlocal means,
- Rotationally invariant similarity measure,
- No-reference image content metric,
- Magnetic resonance image denoising

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References(29)

References

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