Wavelet analysis for compressed image sensing using matrices

Andre Sokolnikov

doi:10.1117/12.2057486

5 May 2014 Wavelet analysis for compressed image sensing using matrices

Andre Sokolnikov

Author Affiliations +

Proceedings Volume 9094, Optical Pattern Recognition XXV; 90940J (2014) https://doi.org/10.1117/12.2057486
Event: SPIE Defense + Security, 2014, Baltimore, Maryland, United States

Abstract

Recently, substantial efforts have been made to find an alternative approach to the Shannon sampling theorem with a method that can deal with large data sets, something for which the Shannon theorem is not easily applicable. If applied, the above approach would have to surmount difficult computational problems resulting from large data. In order to deal with the large data sets, we avoid a universal image acquisition and use wavelet matrices based on tree structures. The proposed approach allows a calculation reduction that yields a better control over the compressed image quality. The suggested technique also advocates a selective approach over the non-adaptive, random functions favored by the Shannon sampling theorem.

Citation Download Citation

Andre Sokolnikov, Andre Sokolnikov, "Wavelet analysis for compressed image sensing using matrices", Proc. SPIE 9094, Optical Pattern Recognition XXV, 90940J (5 May 2014); doi: 10.1117/12.2057486; https://doi.org/10.1117/12.2057486

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