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Denoising geometric image features

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VMV - Variational methods and effective algorithms for imaging and vision

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.

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

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