Median correlation for the analysis of gene expression data
Signal Processing - Special issue: Genomic signal processing
Estimation of FM signal parameters in impulse noise environments
Signal Processing
Statistically-efficient filtering in impulsive environments: weighted myriad filters
EURASIP Journal on Applied Signal Processing
Cost-effective video filtering solution for real-time vision systems
EURASIP Journal on Applied Signal Processing
An effective method for detecting dental diseases by using fast neural networks
SSIP'08 Proceedings of the 8th conference on Signal, Speech and image processing
Enhancement of aerial images using threshold decomposition adaptive morphological filter
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Steerable weighted median filters
IEEE Transactions on Image Processing
A generalized cauchy distribution framework for problems requiring robust behavior
EURASIP Journal on Advances in Signal Processing - Special issue on robust processing of nonstationary signals
One dimensional nonlinear adaptive filters for impulse noise suppression
AEE'06 Proceedings of the 5th WSEAS international conference on Applications of electrical engineering
SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
Hi-index | 35.68 |
Weighted median smoothers, which were introduced by Edgemore in the context of least absolute regression over 100 years ago, have received considerable attention in signal processing during the past two decades. Although weighted median smoothers offer advantages over traditional linear finite impulse response (FIR) filters, it is shown in this paper that they lack the flexibility to adequately address a number of signal processing problems. In fact, weighted median smoothers are analogous to normalized FIR linear filters constrained to have only positive weights. It is also shown that much like the mean is generalized to the rich class of linear FIR filters, the median can be generalized to a richer class of filters admitting positive and negative weights. The generalization follows naturally and is surprisingly simple. In order to analyze and design this class of filters, a new threshold decomposition theory admitting real-valued input signals is developed. The new threshold decomposition framework is then used to develop fast adaptive algorithms to optimally design the real-valued filter coefficients. The new weighted median filter formulation leads to significantly more powerful estimators capable of effectively addressing a number of fundamental problems in signal processing that could not adequately be addressed by prior weighted median smoother structures