Gröbner bases for problem solving in multidimensional systems
Multidimensional Systems and Signal Processing
Two-Dimensional Digital Filters
Two-Dimensional Digital Filters
Multidimensional Digital Signal Processing
Multidimensional Digital Signal Processing
Practical Optimization: Algorithms and Engineering Applications
Practical Optimization: Algorithms and Engineering Applications
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Iterative reweighted l1 design of sparse FIR filters
Signal Processing
Efficient 2-D based algorithms for WLS designs of 2-D FIR filters with arbitrary weighting functions
Multidimensional Systems and Signal Processing
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Is sparsity an issue in 2-D digital filter design problems to explore and why is it important? How a 2-D filter can be designed to retain a desired coefficient sparsity for efficient implementation while achieving best possible performance subject to that sparsity constraint? These are the focus of this paper in which we present a two-phase design method for 2-D FIR digital filters in two most common design settings, namely, the least squares and minimax designs. Simulation studies are presented to illustrate each phase of the proposed design method and to evaluate the performance of the filters designed.