Abstract

One of the main challenges for optimal designs of two-dimensional (2D) finite impulse response (FIR) filters is their heavy computational load due to the large number of filter coefficients and the high dimensions of the data for model fitting. The alternating direction method of multipliers (ADMM) is a powerful technique appropriate for optimization for big data. In this paper, a relaxed ADMM is presented and then applied in the weighted least-squares (WLS) design of linear-phase 2D FIR filters. It is shown that the relaxed ADMM algorithm converges much faster than the standard ADMM algorithm. In addition, a salient feature of the relaxed ADMM is its highly parallel structure which makes it very efficient if implemented in parallel. Simulation examples and comparisons demonstrate the fast convergence and high efficiency of the relaxed ADMM algorithm.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.