Gabor feature based nonlocal means filter for textured image denoising

  • Authors:

  • Shanshan Wang;Yong Xia;Qiegen Liu;Jianhua Luo;Yuemin Zhu;David Dagan Feng

  • Affiliations:

  • School of Biomedical Engineering, Shanghai Jiaotong University, 200240 Shanghai, China and Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologie ...;Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, NSW 2006, Australia;School of Biomedical Engineering, Shanghai Jiaotong University, 200240 Shanghai, China;School of Biomedical Engineering, Shanghai Jiaotong University, 200240 Shanghai, China and College of Aeronautics and Astronautics, Shanghai Jiaotong University, 200240 Shanghai, China;CREATIS, CNRS UMR 5220, Inserm U 630, INSA Lyon, University of Lyon 1, Lyon, France;Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, NSW 2006, Australia and Med-X Research Institute, Shanghai Jiaoton ...

  • Venue:
  • Journal of Visual Communication and Image Representation
  • Year:
  • 2012

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Abstract

The nonlocal means (NLM) filter has distinct advantages over traditional image denoising techniques. However, in spite of its simplicity, the pixel value-based self-similarity measure used by the NLM filter is intrinsically less robust when applied to images with non-stationary contents. In this paper, we use Gabor-based texture features to measure the self-similarity, and thus propose the Gabor feature based NLM (GFNLM) filter for textured image denoising. This filter recovers noise-corrupted images by replacing each pixel value with the weighted sum of pixel values in its search window, where each weight is defined based on the Gabor-based texture similarity measure. The GFNLM filter has been compared to the classical NLM filter and four other state-of-the-art image denoising algorithms in textured images degraded by additive Gaussian noise. Our results show that the proposed GFNLM filter can denoise textured images more effectively and robustly while preserving the texture information.