MR/4: Ridgelet and Curvelet
MR/4 deals with the ridgelet and the curvelet transforms.
Wavelets presents some limitations when the
data present anisotropic features. New methods, such the
ridgelet transform and the curvelet transform,
are better adapted to this kind of data.
Wavelets, Ridgelets and Curvelets can also be combined
in order to benefit of the advantages of each of them.
Applications in MR/4 are
- 2D Ridgelet:
- Ridgelet transform and reconstruction of images.
- Statistics relative to ridgelet coefficients.
- Image Ridgelet filtering.
- 2D Curvelet:
- Curvelet transform and reconstruction of images.
- Statistics relative to curvelets coefficients.
- Curvelet filtering.
- Contrast enhancement by the Curvelet transform.
- Color images and Curvelets:
- RGB image contrast enhancement by the Curvelet transform.
- RGB image Curvelet filtering.
- Combined Filtering Method.
Many examples can be found at
the curvelet home page.
To read further:
- J.-L. Starck, E. Candes, and D.L. Donoho,
"Astronomical Image Representation by the Curvelet Tansform" ,
Astronomy and Astrophysics, in press.
- J.L. Starck, E. Candes, and D.L. Donoho,
"The Curvelet Transform for Image Denoising",
IEEE Transactions on Image Processing ,
11, 6, pp 670 -684, 2002.
- J.L. Starck, "Image Restoration: beyond wavelets", SPIE's
International Technical Group Newsletter on Electronic Imaging,
18, 2, pp 30--40, 2001.
- J.L. Starck, D.L. Donoho and E. Candes, invited paper,
"Very High Quality Image Restoration",
in SPIE conference on Signal and Image Processing: Wavelet Applications
in Signal and Image Processing IX, A. Laine, M.A. Unser and A. Aldroubi Eds,
Vol 4478, 2001.