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Sparse Super-Resolution with Space Matching Pursuits

Abstract : Super-resolution image zooming is possible when the image has some geometric regularity. Directional interpolation algorithms are used in industry, with ad-hoc regularity measurements. Sparse signal decompositions in dictionaries of curvelets or bandlets find indirectly the directions of regularity by optimizing the sparsity. However, super-resolution interpolations in such dictionaries do not outperform cubic spline interpolations. It is necessary to further constraint the sparse representation, which is done through projections over structured vector spaces. A space matching pursuit algorithm is introduced to compute image decompositions over spaces of bandlets, from which a super-resolution image zooming is derived. Numerical experiments illustrate the efficiency of this super-resolution procedure compared to cubic spline interpolations.
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Submitted on : Friday, March 20, 2009 - 3:04:18 PM
Last modification on : Wednesday, October 20, 2021 - 12:23:57 AM
Long-term archiving on: : Friday, October 12, 2012 - 2:01:57 PM


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  • HAL Id : inria-00369620, version 1



Guoshen Yu, Stéphane Mallat. Sparse Super-Resolution with Space Matching Pursuits. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Inria Rennes - Bretagne Atlantique, Apr 2009, Saint Malo, France. ⟨inria-00369620⟩



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