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Denoising Geometric Image Features

Presented by: 
Stacey Levine Duquesne University
Date: 
Tuesday 31st October 2017 - 09:00 to 09:50
Venue: 
INI Seminar Room 1
Abstract: 
Given a noisy image, it can sometimes be more productive to denoise a transformed version of the image rather than process the image data directly. In this talk we will discuss several novel frameworks for image denoising, including one that involves smoothing the noisy image’s level line curvature and another that regularizes the components of the noisy image in a moving frame that encodes its local geometry. Both frameworks satisfy some nice unexpected properties that provide justification for this framework. Experiments confirm an improvement over the usual denoising paradigm in terms of both PSNR and SSIM. Moreover, this approach provides a mechanism for preserving geometry in solutions of sparse patch based models that typically exploit self similarity. This is joint work with Thomas Batard, Marcelo Bertalmio, and Gabriela Ghimpeteanu.
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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons