DCC '01 Proceedings of the Data Compression Conference
A new method of estimating wavelet with desired features from a given signal
Signal Processing - Content-based image and video retrieval
A hybrid parallel projection approach to object-based image restoration
Pattern Recognition Letters
Denoising of aerial imagery using higher-order statistics
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
Wavelet-based diffusion approaches for signal denoising
Signal Processing
Denoising by sparse approximation: error bounds based on rate-distortion theory
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
Denoising swallowing sound to improve the evaluator's qualitative analysis
Computers and Electrical Engineering
Representation and compression of multidimensional piecewise functions using surflets
IEEE Transactions on Information Theory
A short introduction to wavelets and their applications
IEEE Circuits and Systems Magazine
A SURE approach for digital signal/image deconvolution problems
IEEE Transactions on Signal Processing
Noisy data and impulse response estimation
IEEE Transactions on Signal Processing
Multiple target localization using compressive sensing
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Best basis denoising with non-stationary wavelet packets
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
On the statistics of matching pursuit angles
Signal Processing
Noiseless codelength in wavelet denoising
EURASIP Journal on Advances in Signal Processing
Bayesian marginal statistics for speech enhancement using log Gabor wavelet
International Journal of Speech Technology
Hi-index | 754.90 |
We propose a best basis algorithm for signal enhancement in white Gaussian noise. The best basis search is performed in families of orthonormal bases constructed with wavelet packets or local cosine bases. We base our search for the “best” basis on a criterion of minimal reconstruction error of the underlying signal. This approach is intuitively appealing, because the enhanced or estimated signal has an associated measure of performance, namely, the resulting mean-square error. Previous approaches in this framework have focused on obtaining the most “compact” signal representations, which consequently contribute to effective denoising. These approaches, however, do not possess the inherent measure of performance which our algorithm provides. We first propose an estimator of the mean-square error, based on a heuristic argument and subsequently compare the reconstruction performance based upon it to that based on the Stein (1981) unbiased risk estimator. We compare the two proposed estimators by providing both qualitative and quantitative analyses of the bias term. Having two estimators of the mean-square error, we incorporate these cost functions into the search for the “best” basis, and subsequently provide a substantiating example to demonstrate their performance