Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Multiplicative noise removal via a learned dictionary

Language

English

Abstract

Multiplicative noise removal is a challenging image processing problem, and most existing methods are based on the maximum a posteriori formulation and the logarithmic transformation of multiplicative denoising problems into additive denoising problems. Sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, in this paper, we propose to learn a dictionary from the logarithmic transformed image, and then to use it in a variational model built for noise removal. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio, and mean absolute deviation error, the proposed algorithm outperforms state-of-the-art methods. © 2012 IEEE.

Keywords

Denoising, dictionary, multiplicative noise, sparse representation, variational model

Publication Date

2012

Source Publication Title

IEEE Transactions on Image Processing

Volume

21

Issue

11

Start Page

4534

End Page

4543

Publisher

Institute of Electrical and Electronics Engineers

DOI

10.1109/TIP.2012.2205007

Link to Publisher's Edition

http://dx.doi.org/10.1109/TIP.2012.2205007

ISSN (print)

10577149

ISSN (electronic)

19410042

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