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Anisotropic Spatiotemporal Regularization in Compressive Video Recovery by Adaptively Modeling the Residual Errors as Correlated Noise

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Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2018 - Proceedings
KustantajaIEEE
ISBN (painettu)9781538609514
DOI - pysyväislinkit
TilaJulkaistu - 27 elokuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE Image, Video, and Multidimensional Signal Processing Workshop - Zagori, Kreikka
Kesto: 10 kesäkuuta 201812 kesäkuuta 2018

Conference

ConferenceIEEE Image, Video, and Multidimensional Signal Processing Workshop
MaaKreikka
KaupunkiZagori
Ajanjakso10/06/1812/06/18

Tiivistelmä

Many approaches to compressive video recovery proceed iteratively, treating the difference between the previous estimate and the ideal video as residual noise to be filtered. We go beyond the common white-noise modeling by adaptively modeling the residual as stationary spatiotemporally correlated noise. This adaptive noise model is updated at each iteration and is highly anisotropic in space and time; we leverage it with respect to the transform spectra of a motion-compensated video denoiser. Experimental results demonstrate that our proposed adaptive correlated noise model outperforms state-of-the-art methods both quantitatively and qualitatively.

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