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Second Order Differences for Combined Cyclic and Vector Space Data and their Application to Denoising and Inpainting using a TV-type Approach

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If you have a question about this talk, please contact Dr Jan Lellmann .

In many image applications, we have to deal with noisy measurements or even lossy data. In many cases, image data is vectorial, e.g. for color images, or even a combination of cyclic and linear space data, like for the HSV color space. In order to restore real valued data, TV minimization is quite popular. Extending this to higher order differences avoids the so called `stair casing` effect.

This talk introduce second order differences for combined cyclic and linear space data, like HSV . We present a second order total variation type functional for the inpainting and the denoising situation and the combination of both. We further derive and efficient algorithm to minimize this functional and apply the algorithms to concrete problems.

This talk is part of the Cambridge Image Analysis Seminars series.

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