I Can't Believe It's Not Lena !

Finally, some theory for TV!

Stable image reconstruction using total variation minimization by Deanna Needell, Rachel Ward. The abstract reads:

This article presents near-optimal guarantees for accurate and robust image recovery from under-sampled noisy measurements using total variation minimization, and our results may be the first of this kind. In particular, we show that from O(slog(N)) nonadaptive linear measurements, an image can be reconstructed to within the best s-term approximation of its gradient, up to a logarithmic factor.. Along the way, we prove a strengthened Sobolev inequality for functions lying in the null space of a suitably incoherent matrix.

I note the authors' painstaking effort at getting the full permission for getting fair use material :-) Laurent Jacques wonders about the connection between this work and co-sparsity.


Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.