Learning optimal wavelets from overcomplete representations

Hamid Eghbalnia; Amir H. Assadi

doi:10.1117/12.408612

4 December 2000 Learning optimal wavelets from overcomplete representations

Hamid Eghbalnia, Amir H. Assadi

Proceedings Volume 4119, Wavelet Applications in Signal and Image Processing VIII; (2000) https://doi.org/10.1117/12.408612
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

Abstract

Efficient and robust representation of signals has been the focus of a number of areas of research. Wavelets represent one such representation scheme that enjoys desirable qualitites such as time-frequency localization. Once the Mother wavelet has been selected, other wavelets can be generated as translated and dilated versions of the Mother wavelet in the 1D case. In the 2D case tensor product of two 1D wavelets is the most often used transform. Over complete representation of wavelets has proved to be of great advantage, both in sparse coding of complex scenes and multi-media data compression. On the other hand over completeness raises a number of technical difficulties for robust computation and systematic generalization of constructions beyond their original application domains.

Citation Download Citation

Hamid Eghbalnia and Amir H. Assadi "Learning optimal wavelets from overcomplete representations", Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); https://doi.org/10.1117/12.408612

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