For the multiresolution analysis and its detail spaces , take the completed tensor product . Since ,
The three tensor-product wavelets are
Taking products of members of the two one-dimensional families gives an orthonormal basis of each respective completed tensor product. Density and the coarse-scale trivial intersection yield
as an orthonormal basis of . The normalization is in two dimensions. On the square, tensorize the interval-adapted wavelet basis and include the finite coarse scaling function part.
Use localized tensor-product wavelets and finitely many bounded polynomial pieces, with the boundary wavelets adapted as in part (b). A rectifiable smooth curve of length meets dyadic squares of side : subdividing an arclength parametrization into pieces of length at most covers it by that many balls, each meeting only a bounded number of squares. Enlarging squares by the fixed support diameter preserves the count.
Each normalized two-dimensional wavelet has norm . A coefficient meeting the curve is therefore , and the total squared energy of these coefficients at level is . If , all other coefficients vanish by the vanishing moments. Keeping the curve coefficients through level costs and leaves squared error .
The printed part (e) does not repeat . The bound still holds for any fixed polynomial degree when : on a smooth piece, a Taylor polynomial in the variable carrying a wavelet gives coefficient size . There are such coefficients, so their squared energy is . Retain all coefficients through , and curve coefficients through . The cost is and the omitted squared energy is
The best N-term approximation is at least as good as this selection, proving
This argument covers the unqualified finite-degree clause without adding an unnecessary restriction .