Curvelet imaging and processing: sparseness-constrained least-squares migration
Title | Curvelet imaging and processing: sparseness-constrained least-squares migration |
Publication Type | Conference |
Year of Publication | 2004 |
Authors | Felix J. Herrmann, Peyman P. Moghaddam |
Conference Name | CSEG Annual Conference Proceedings |
Month | 05 |
Publisher | CSEG |
Keywords | Presentation, SLIM |
Abstract | A non-linear edge-preserving solution to the least-squares migration problem with sparseness constraints is introduced. The applied formalism explores Curvelets as basis functions that, by virtue of their sparseness and locality, not only allow for a reduction of the dimensionality of the imaging problem but which also naturally lead to a non-linear solution with significantly improved signal-to-noise ratio. Additional conditions on the image are imposed by solving a constrained optimization problem on the estimated Curvelet coefficients initialized by thresholding. This optimization is designed to also restore the amplitudes by (approximately) inverting the normal operator, which is, like-wise to the (de)-migration operators, almost diagonalized by the Curvelet transform. |
URL | https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Herrmann04CSEGcia2/Herrmann04CSEGcia2_paper.pdf |
Presentation | |
Citation Key | herrmann2004CSEGcia2 |