Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  analyze the wavelet shrinkage algorithm of Donoho and Johnstone (1994) in order to assess the quality of the reconstruction of a signal obtained from noisy samples.  give a deviation estimate for the maximum squared error (and, consequently, for the average squared error), under the assumption that the signal comes from a Holder class, and the noise samples are independent, of zero mean, and bounded. Our main technique is Talagrand's (1995) isoperimetric theorem. Our result shows a better behavior of the wavelet shrinkage.
['Dawei Hong', 'Jean-Camille Birget']
I am writing a research paper on a idea Deviation bounds for wavelet shrinkage
[{"from": "human", "value": "I am writing a research paper on a idea Deviation bounds for wavelet shrinkage"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  analyze the wavelet shrinkage algorithm of Donoho and Johnstone (1994) in order to assess the quality of the reconstruction of a signal obtained from noisy samples.  give a deviation estimate for the maximum squared error (and, consequently, for the average squared error), under the assumption that the signal comes from a Holder class, and the noise samples are independent, of zero mean, and bounded. Our main technique is Talagrand's (1995) isoperimetric theorem. Our result shows a better behavior of the wavelet shrinkage."}]