Constrained signal reconstruction from wavelet transform coefficients
A new method is introduced for reconstructing a signal from an incomplete sampling of its Discrete Wavelet Transform (DWT). The algorithm yields a minimum-norm estimate satisfying a priori upper and lower bounds on the signal. The method is based on a finite-dimensional representation theory for minimum-norm estimates of bounded signals developed by R.E. Cole. Cole`s work has its origins in earlier techniques of maximum-entropy spectral estimation due to Lang and McClellan, which were adapted by Steinhardt, Goodrich and Roberts for minimum-norm spectral estimation. Cole`s extension of their work provides a representation for minimum-norm estimates of a class of generalized transforms in terms of general correlation data (not just DFT`s of autocorrelation lags, as in spectral estimation). One virtue of this great generality is that it includes the inverse DWT. 20 refs.
- Research Organization:
- Los Alamos National Lab., NM (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 10123902
- Report Number(s):
- LA-UR--92-45; CONF-920354--4; ON: DE92007423
- Country of Publication:
- United States
- Language:
- English
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