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Denoising Geometric Image Features

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VMVW02 - Generative models, parameter learning and sparsity

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.

This talk is part of the Isaac Newton Institute Seminar Series series.

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