3-D Data Denoising and Inpainting with the Low-Redundancy Fast Curvelet Transform |
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Authors: | A Woiselle J-L Starck J Fadili |
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Affiliation: | 1.CEA, IRFU, SEDI-SAP, Laboratoire Astrophysique des Interactions Multi-échelles (UMR 7158),CEA/DSM-CNRS-Universite Paris Diderot,Gif-Sur-Yvette,France;2.Sagem groupe SAFRAN,Argenteuil CEDEX,France;3.Image Processing Group, ENSICAEN,GREYC CNRS UMR 6072,Caen Cedex,France |
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Abstract: | In this paper, we first present a new implementation of the 3-D fast curvelet transform, which is nearly 2.5 less redundant
than the Curvelab (wrapping-based) implementation as originally proposed in Ying et al. (Proceedings of wavelets XI conference,
San Diego, 2005) and Candès et al. (SIAM Multiscale Model. Simul. 5(3):861–899, 2006), which makes it more suitable to applications including massive data sets. We report an extensive comparison in denoising
with the Curvelab implementation as well as other 3-D multi-scale transforms with and without directional selectivity. The
proposed implementation proves to be a very good compromise between redundancy, rapidity and performance. Secondly, we exemplify
its usefulness on a variety of applications including denoising, inpainting, video de-interlacing and sparse component separation.
The obtained results are good with very simple algorithms and virtually no parameter to tune. |
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