Fast Iterative Adaptive Multi-quadric Radial Basis Function Method for Edges Detection of Piecewise Functions—I: Uniform Mesh
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In Jung et al. (Appl Numer Math 61:77–91, 2011), an iterative adaptive multi-quadric radial basis function (IAMQ-RBF) method has been developed for edges detection of the piecewise analytical functions. For a uniformly spaced mesh, the perturbed Toeplitz matrices, which are modified by those columns where the shape parameters are reset to zero due to the appearance of edges at the corresponding locations, are created. Its inverse must be recomputed at each iterative step, which incurs a heavy \(O(n^3)\) computational cost. To overcome this issue of efficiency, we develop a fast direct solver (IAMQ-RBF-Fast) to reformulate the perturbed Toeplitz system into two Toeplitz systems and a small linear system via the Sherman–Morrison–Woodbury formula. The \(O(n^2)\) Levinson–Durbin recursive algorithm that employed Yule–Walker algorithm is used to find the inverse of the Toeplitz matrix fast. Several classical benchmark examples show that the IAMQ-RBF-Fast based edges detection method can be at least three times faster than the original IAMQ-RBF based one. And it can capture an edge with fewer grid points than the multi-resolution analysis (Harten in J Comput Phys 49:357–393, 1983) and just as good as if not better than the L1PA method (Denker and Gelb in SIAM J Sci Comput 39(2):A559–A592, 2017). Preliminary results in the density solution of the 1D Mach 3 extended shock–density wave interaction problem solved by the hybrid compact-WENO finite difference scheme with the IAMQ-RBF-Fast based shocks detection method demonstrating an excellent performance in term of speed and accuracy, are also shown.
KeywordsMulti-quadric radial basis function Edges detection Toeplitz matrix Sherman–Morrison–Woodbury Levinson–Durbin Yule–Walker
The authors would like to acknowledge Prof. Jianlin Xia for valuable discussion on the superfast methods for solving the Toeplitz matrix, and Prof. Tom Goldstein for sharing the codes of the Split Bregman method for reconstructing images from a subset of Fourier coefficients using total-variation regularization. The authors would like to acknowledge the funding support of this research by National Science and Technology Major Project (J-GFZX020101010.4), Shandong Provincial Natural Science Foundation (ZR2017MA016), National Natural Science Foundation of China (41306002) and Fundamental Research Funds for the Central Universities (201562012). The author (Don) also likes to thank the Ocean University of China for providing the startup fund (201712011) that is used in supporting this work.
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