Efficient Filter Flow for Space-Variant Multiframe Blind Deconvolution

Hirsch, M; Sra, S; Schölkopf, B; Harmeling, S

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Efficient Filter Flow for Space-Variant Multiframe Blind Deconvolution

MPG-Autoren

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83969

Hirsch, M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons76142

Sra, S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83954

Harmeling, S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Hirsch, M., Sra, S., Schölkopf, B., & Harmeling, S.(2009). Efficient Filter Flow for Space-Variant Multiframe Blind Deconvolution (188).

Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C21C-A

Zusammenfassung

Ultimately being motivated by facilitating space-variant blind deconvolution, we present a class of linear transformations, that are expressive enough for space-variant filters, but at the same time especially designed for efficient matrix-vector-multiplications. Successful results on astronomical imaging through atmospheric turbulences and on noisy magnetic resonance images of constantly moving objects demonstrate the practical significance of our approach.