Probabilistic Non-Local Means

In this letter, we propose a so-called probabilistic non-local means (PNLM) method for image denoising. Our main contributions are: 1) we point out defects of the weight function used in the classic NLM; 2) we successfully derive all theoretical statistics of patch-wise differences for Gaussian nois...

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Bibliographic Details
Published in:IEEE signal processing letters 2013-08, Vol.20 (8), p.763-766
Main Authors: Yue Wu, Tracey, B., Natarajan, P., Noonan, J. P.
Format: Article
Language:English
Subjects:
Adaptive algorithm
Coplanar waveguides
Image denoising
Noise
Noise measurement
Noise reduction
non-local means
Probabilistic logic
probabilistic modeling
Signal processing algorithms
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Summary:In this letter, we propose a so-called probabilistic non-local means (PNLM) method for image denoising. Our main contributions are: 1) we point out defects of the weight function used in the classic NLM; 2) we successfully derive all theoretical statistics of patch-wise differences for Gaussian noise; and 3) we employ this prior information and formulate the probabilistic weights truly reflecting the similarity between two noisy patches. Our simulation results indicate the PNLM outperforms the classic NLM and many NLM recent variants in terms of the peak signal noise ratio (PSNR) and the structural similarity (SSIM) index. Encouraging improvements are also found when we replace the NLM weights with the PNLM weights in tested NLM variants.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2013.2263135