Signal recovery in compressive sensing via multiple sparsifying bases
Wijewardhana, U. L.; Belyaev, E.; Codreanu, M.; Latva-aho, M. (2017-05-11)
U. L. Wijewardhana, E. Belyaev, M. Codreanu and M. Latva-Aho, "Signal Recovery in Compressive Sensing via Multiple Sparsifying Bases," 2017 Data Compression Conference (DCC), Snowbird, UT, 2017, pp. 141-150. doi: 10.1109/DCC.2017.37
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe202003208625
Tiivistelmä
Abstract
Compressive sensing theory asserts that, under certain conditions, a high dimensional but compressible signal can be recovered from a small number of random linear projections by utilizing computationally efficient algorithms. The a priori knowledge of the basis in which the signal of interest is sparse is the key assumption utilized by such algorithms. However, the basis in which the signal is the sparsest is unknown for many natural signals of interest. Instead there may exist multiple bases which lead to a compressible representation of the signal: e.g., an image is compressible in different wavelet transforms. We show that a significant performance improvement can be achieved by utilizing multiple estimates of the signal using sparsifying bases in the context of signal reconstruction from compressive samples. Further, we derive a customized interior-point method to jointly obtain multiple estimates of a 2-D signal (image) from compressive measurements utilizing multiple sparsifying bases as well as the fact that the images usually have a sparse gradient.
Kokoelmat
- Avoin saatavuus [31657]