Gabor feature based nonlocal means filter for textured image denoising

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• 作者：Shanshan Wang ; Yong Xia ; Qiegen Liu ; Jianhua Luo ; Yuemin Zhu ; David Dagan Feng
• 关键词：Nonlocal means filter ; Gabor filter ; Image denoising ; Textured image analysis ; Feature extraction ; Similarity detection ; Signal restoration ; Gaussian noise
• 刊名：Journal of Visual Communication and Image Representation
• 出版年：2012
• 出版时间：October, 2012
• 年：2012
• 卷：23
• 期：7
• 页码：1008-1018
• 全文大小：2379 K

文摘

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.