Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8551
Title: Gabor feature based nonlocal means filter for textured image denoising
Authors: Wang, S
Xia, Y
Liu, Q
Luo, J
Zhu, Y
Feng, DD
Keywords: Nonlocal means filter
Gabor filter
Image denoising
Textured image analysis
Feature extraction
Similarity detection
Signal restoration
Gaussian noise
Issue Date: 2012
Publisher: Academic Press
Source: Journal of visual communication and image representation, 2012, v. 23, no. 7, p. 1008-1018 How to cite?
Journal: Journal of visual communication and image representation 
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.
URI: http://hdl.handle.net/10397/8551
ISSN: 1047-3203
DOI: 10.1016/j.jvcir.2012.06.011
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