Probabilistic Non-Local Means

Author

Yue Wu ; Tracey, Brian ; Natarajan, Prem ; Noonan, J.P.

Author_Institution

Tufts Univ., Medford, MA, USA

Volume

20

Issue

8

fYear

2013

fDate

Aug. 2013

Firstpage

763

Lastpage

766

Abstract

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.

Keywords

Gaussian noise; image denoising; probability; Gaussian noise; PNLM method; PSNR; SSIM; image denoising; noisy patches; patch-wise differences; peak signal noise ratio; probabilistic nonlocal means method; probabilistic weights; structural similarity index; weight function; Coplanar waveguides; Image denoising; Noise; Noise measurement; Noise reduction; Probabilistic logic; Signal processing algorithms; Adaptive algorithm; image denoising; non-local means; probabilistic modeling;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2013.2263135

Filename

6516064