Abstract
In multifractal denoising techniques, the acuracy of the Hölder exponents estimations is crucial for the quality of the outputs. In continuity with the method described in [1], where a wavelet decomposition was used, we investigate the use of another Hölder exponent estimation technique, based on the analysis of the local “oscillations” of the signal. The associated inverse problem to be solved, i.e. finding the signal which is the closest to the initial noisy one but having the prescribed regularity, is then more complex. Moreover, the associated search space is of a different nature as in [1], which necessitates the design of ad-hoc genetic operators.
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Legrand, P., Lutton, E., Olague, G. (2006). Evolutionary Denoising Based on an Estimation of Hölder Exponents with Oscillations. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_49
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DOI: https://doi.org/10.1007/11732242_49
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