Super-Resolution with Adaptive Regularization

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Super-Resolution with Adaptive Regularization
Multi-channel super-resolution is a means of recovering high frequency information by trading off the temporal bandwidth. Almost all the methods proposed in the literature are based on optimizing a cost function. But since the problem is usually ill-posed, one needs to impose some regularity constraints. However, regularity constraints tend to attenuate high frequency contents of data (usually present in the form of discontinuities). This inherent contradiction between regularization and super-resolution has not been addressed in the literature, despite the availability of off the shelf tools. I n this paper, we have investigated this issue in the context of adaptive regularization, using @-functions(convex, non-convex, bounded, unbounded).
Anne Lorette, Hassan Shekarforoush, Josiane Zerubi
Added 26 Oct 2009
Updated 26 Oct 2009
Type Conference
Year 1997
Where ICIP
Authors Anne Lorette, Hassan Shekarforoush, Josiane Zerubia
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