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<p>Discrete formulation of the surface interpolation problem usually leads to a large sparse linear equation system. Due to the poor convergence condition of the equation system, the convergence rate of solving this problem with iterative method is very slow. To improve this condition, a multiresolution basis transfer scheme based on the wavelet transform is proposed. By applying the wavelet transform, the original interpolation basis is transformed into two sets of bases with larger supports while the admissible solution space remains unchanged. With this basis transfer, a new set of nodal variables results and an equivalent equation system with better convergence condition can be solved. The basis transfer can be easily implemented by using an QMF matrix pair associated with the chosen interpolation basis. The consequence of the basis transfer scheme can be regarded as a preconditioner to the subsequent iterative computation method. The effect of the transfer is that the interpolated surface is decomposed into its low-frequency and high-frequency portions in the frequency domain. It has been indicated that the convergence rate of the interpolated surface is dominated by the low-frequency portion. With this frequency domain decomposition, the low-frequency portion of the interpolated surface can be emphasized. As compared with other acceleration methods, this basis transfer scheme provides a more systematical approach for fast surface interpolation. The easy implementation and high flexibility of the proposed algorithm also make it applicable to various regularization problems.</p>
interpolation; convergence of numerical methods; wavelet transforms; signal processing; surface interpolation; multiresolution wavelet transform; large sparse linear equation system; convergence; multiresolution basis transfer scheme; interpolation basis; QMF matrix pair; preconditioner; frequency domain

W. Chang and M. Yaou, "Fast Surface Interpolation using Multiresolution Wavelet Transform," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 673-688, 1994.
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