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Removing Speckle Noise by Analysis Dictionary Learning

Lookup NU author(s): Professor Jonathon Chambers


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Speckle noise inherently exists in images acquired by coherent systems, for example, synthetic aperture radar (SAR) and sonar images. Removal of speckle noise is a challenging problem because the noise multiplies (rather than adds to) the original image and it does not follow a Gaussian distribution. In this paper, we focus on the speckle noise removal problem and propose a method using analysis dictionary learning. In our proposed method, the image recovery is addressed in the logarithmic transform domain, thereby converting the multiplicative model to an additive model. Our formulation consists of a data fidelity term derived from the distribution of the speckle noise and a regularization term using the learned analysis dictionary. Experimental results on synthetic speckled images and real SAR images demonstrate the promising performance of the proposed method.

Publication metadata

Author(s): Dong J, Wang WW, Chambers J

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 5th Sensor Signal Processing for Defence Conference (SSPD)

Year of Conference: 2015

Pages: 132-136

Online publication date: 05/10/2015

Acceptance date: 01/01/1900

Publisher: Institute of Electrical and Electronics Engineers


DOI: 10.1109/SSPD.2015.7288521

Library holdings: Search Newcastle University Library for this item

Series Title: Sensor Signal Processing for Defence (SSPD), 2015

ISBN: 9781479974443