Abstract
A concept for shrinking of wavelet coefficients has been presented and explored in a series of articles [2–4]. The theory and experiments so far suggest a strategy where the shrinking adapts to local smoothness properties of the original signal. From this strategy we employ partitioning of the global signal and local shrinking under smoothness constraints. Furthermore, we benchmark shrinking of local partitions’ wavelet coefficients utilizing a selection of wavelet basis functions. Then we present and benchmark an adaptive partition-based shrinking strategy where the best performing shrinkage is applied to individual partitions, one at a time. Finally, we compare the local and global benchmark results.
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Dalmo, R., Bratlie, J., Bang, B. (2015). Performance of a Wavelet Shrinking Method. In: Dimov, I., Fidanova, S., Lirkov, I. (eds) Numerical Methods and Applications. NMA 2014. Lecture Notes in Computer Science(), vol 8962. Springer, Cham. https://doi.org/10.1007/978-3-319-15585-2_29
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DOI: https://doi.org/10.1007/978-3-319-15585-2_29
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