Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  investigates the asymptotic behavior of the minimum risk threshold for wavelet coefficients with additive, homoscedastic, Gaussian noise and for a soft-thresholding scheme.  start from N samples from a signal on a continuous time axis. For piecewise smooth signals and for N/spl rarr//spl infin/, this threshold behaves as C/spl radic/(2logN)/spl sigma/, where /spl sigma/ is the noise standard-deviation. The paper contains an original proof for this asymptotic behavior as ll as an intuitive explanation. The paper also discusses the importance of this asymptotic behavior for practical cases when  estimate the minimum risk threshold.
['Maarten Jansen', 'Adhemar Bultheel']
I am writing a research paper on a idea Asymptotic behavior of the minimum mean squared error threshold for noisy wavelet coefficients of piecewise smooth signals
[{"from": "human", "value": "I am writing a research paper on a idea Asymptotic behavior of the minimum mean squared error threshold for noisy wavelet coefficients of piecewise smooth signals"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  investigates the asymptotic behavior of the minimum risk threshold for wavelet coefficients with additive, homoscedastic, Gaussian noise and for a soft-thresholding scheme.  start from N samples from a signal on a continuous time axis. For piecewise smooth signals and for N/spl rarr//spl infin/, this threshold behaves as C/spl radic/(2logN)/spl sigma/, where /spl sigma/ is the noise standard-deviation. The paper contains an original proof for this asymptotic behavior as ll as an intuitive explanation. The paper also discusses the importance of this asymptotic behavior for practical cases when  estimate the minimum risk threshold."}]