Abstract
We study image approximation by a separable wavelet basis \( \{\psi(2^{k_1}x-i)\psi(2^{k_2}y-j), \phi(x-i)\psi(2^{k_2}y-j), \psi(2^{k_1}(x-i)\phi(y-j), \phi(x-i)\phi(y-i)\},$ where $k_1, k_2 \in \mathbb{Z}_+; i,j\in\mathbb{Z}; \) and ϕ,ψ are elements of a standard biorthogonal wavelet basis in L2(ℝ). Because k1≠ k2, the supports of the basis elements are rectangles, and the corresponding transform is known as the rectangular wavelet transform. We provide a self-contained proof that if one-dimensional wavelet basis has M dual vanishing moments then the rate of approximation by N coefficients of rectangular wavelet transform is \( \mathcal{O}(N^{-M}) \) for functions with mixed derivative of order M in each direction. These results are consistent with optimal approximation rates for such functions. The square wavelet transform yields the approximation rate is \( \mathcal{O}(N^{-M/2}) \) for functions with all derivatives of the total order M. Thus, the rectangular wavelet transform can outperform the square one if an image has a mixed derivative. We provide experimental comparison of image approximation which shows that rectangular wavelet transform outperform the square one.
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Vyacheslav Zavadsky got his M.Sc. (with distinction) in computer science and applied mathematics from Belarusian State University in 1994 and his Ph.D. in mathematics and statistics in 1998 from Belarusian Academy of Sciences and Belarusian State University. He worked at Institute of Mathematics of Belarusian Academy of sciences, and Belarusian center for medical technologies. He also held progressively responsible technical and research positions in the industry: at MZOR, eBusiness technologies, and Webmotion. At present, he is the principal software architect with Semiconductor insights. His research interests include mathematical and statistical methods in vision; machine learning, and structural data mining. Vyacheslav is author of more then ten peer reviewed papers and conference presentation, and 7 pending inventions.
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Zavadsky, V. Image Approximation by Rectangular Wavelet Transform. J Math Imaging Vis 27, 129–138 (2007). https://doi.org/10.1007/s10851-007-0777-z
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DOI: https://doi.org/10.1007/s10851-007-0777-z