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- W49972091 abstract "Denoising is critical in many signal applications in which noise contamination reduces the performance of signal processing. For example, signal analysis often results in incorrect characterization because of noise. In signal compression, contaminated signals are often difficult to compress because their entropy values are very high. The aim of this chapter is to present a new framework for signal denoising based on multifractality, and demonstrates its practicality with several examples. Signal denoising is concerned with the separation of noise from a signal, and then with reducing the noise without altering the signal significantly. It demonstrates that a multifractal measure can be used to guide the process of noise reduction so that the fractal spectrum is preserved in the signal." @default.
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- W49972091 date "1996-01-01" @default.
- W49972091 modified "2023-09-26" @default.
- W49972091 title "Signal Denoising through Multifractality" @default.
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- W49972091 doi "https://doi.org/10.1016/b978-044482587-2/50089-6" @default.
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