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Improving nonlocal means Method via a No-reference Image Content Metric for MRI Denoising

Chinese Journal of Electronics 2014年第4期23卷 735-741页

作者： HOU Xin LI Jianwu LU Yao DONG Zhengchao Beijing Key Laboratory of Intelligent Information Technology School of Computer Science and TechnologyBeijing Institute of Technology Brain Imaging Lab Columbia University

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.

关键词： nonlocal means Rotationally invariant similarity measure No-reference image content metric Magnetic resonance image denoising

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Filter Bank Based nonlocal means for Denoising Magnetic Resonance Images

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Journal of Shanghai Jiaotong university(Science) 2014年第1期19卷 72-78页

作者：郭甜莉刘且根骆建华 School of Aeronautics and Astronautics Shanghai Jiaotong University

Image denoising is a classical problem in image processing. Its essential goal is to preserve the image features and to reduce noise effiectively. The nonlocal means(NL-means) filter is a successful approach proposed in recent years due to its patch similarity comparison. However, the accuracy of similarities in this algorithm degrades when it suffiers from heavy noise. In this paper, we introduce feature similarities based on a multichannel filter into NL-means filter. The multi-bank based feature vectors of each pixel in the image are computed by convolving from various orientations and scales to Leung-Malik set(edge, bar and spot filters), and then the similarities based on this information are computed instead of pixel intensity. Experiments are carried out with Rician noise. The results demonstrate the superior performance of the proposed method. The wavelet-based method and traditional NL-means in term of both mean square error(MSE) and perceptual quality are compared with the proposed method, and structural similarity(SSIM) and quality index based on local variance(QILV) are given.

关键词： Leung-Malik(LM) filter nonlocal means similarity image denoising Rician noise

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Seismic random noise suppression using an adaptive nonlocal means algorithm

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Applied Geophysics 2013年第1期10卷 33-40,117,118页

作者：尚帅韩立国吕庆田谭尘青吉林大学地球探测科学与技术学院吉林长春130026 中国地质科学院北京100000

nonlocal means filtering is a noise attenuation method based on redundancies in image information. It is also a nonlocal denoising method that uses the self-similarity of an image, assuming that the valid structures of the image have a certain degree of repeatability that the random noise lacks. In this paper, we use nonlocal means filtering in seismic random noise suppression. To overcome the problems caused by expensive computational costs and improper filter parameters, this paper proposes a block-wise implementation of the nonlocal means method with adaptive filter parameter estimation. Tests with synthetic data and real 2D post-stack seismic data demonstrate that the proposed algorithm better preserves valid seismic information and has a higher accuracy when compared with traditional seismic denoising methods （e.g., f-x deconvolution）, which is important for subsequent seismic processing and interpretation.

关键词： seismic prospecting adaptive nonlocal means random noise attenuation

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Image Denoising Algorithm Considering nonlocal Texture Pattern

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Journal of Measurement Science and Instrumentation 2011年第3期2卷 247-250页

作者： Sang-wook PARK Moon-gi KANG The Institute of Telecommunication-Multimedia-Soc Information Technology Yonsei University

Image denoising is indispensable for image *** this paper,image denoising algorithm based on nonlocal means(NLM)filter is ***,abundant enhancements based on NLM filter have been ***,the performance of NLM filter is still inferior to that of other image processing approaches such as *** this paper,NLM algorithm with weight refinement is utilized for image *** refinement is performed to thoroughly take advantage of self-similarity of the *** results show good performance of the proposed method.

关键词： image denoising nonlocal means texture pattern weight refinement weight re-ordering

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