A Fast Algorithm to Map Functions Forward

Abstract

Mapping functions forward is required in image warping and other signal processing applications. The problem is described as follows: specify an integer d ≥ 1, a compact domain D ⊂ Rd, lattices L1, L2 ⊂ Rd, and a deformation function F : D → Rd that is continuously differentiable and maps D one-to-one onto F(D). Corresponding to a function J : F(D) → R, define the function I = J ○ F. The forward mapping problem consists of estimating values of J on L2 ∩ F(D), from the values of I and F on L1 ∩ D. Forward mapping is difficult, because it involves approximation from scattered data (values of I ○ F-1 on the set F(L1 ∩ D)), whereas backward mapping (computing I from J) is much easier because it involves approximation from regular data (values of J on L2 ∩ D). We develop a fast algorithm that approximates J by an orthonormal expansion, using scaling functions related to Daubechies wavelet bases. Two techniques for approximating the expansion coefficients are described and numerical results for a one dimensional problem are used to illustrate the second technique. In contrast to conventional scattered data interpolation algorithms, the complexity of our algorithm is linear in the number of samples.