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Denoising geometric image features

Presented by: 
Stacey Levine Duquesne University
Thursday 26th October 2017 - 15:30 to 16:30
INI Seminar Room 1
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 two novel frameworks for image denoising, one that involves denoising 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 cases satisfy nice unexpected properties that provide justification for this framework. Experiments confirm the improvement when using this approach in terms of both PSNR and SSIM as well as visually.
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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons