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JMIV
2011

3-D Data Denoising and Inpainting with the Low-Redundancy Fast Curvelet Transform

8 years 6 months ago
3-D Data Denoising and Inpainting with the Low-Redundancy Fast Curvelet Transform
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 [1, 2], 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.
A. Woiselle, Jean-Luc Starck, Jalal Fadili
Added 14 May 2011
Updated 14 May 2011
Type Journal
Year 2011
Where JMIV
Authors A. Woiselle, Jean-Luc Starck, Jalal Fadili
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