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Complex Wavelet Bases, Steerability, and the Marr-Like Pyramid

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2 Author(s)
Van De Ville, D. ; Biomed. Imaging Group (BIG), Ecole Polytech- nique Federate de Lausanne (EPFL), Lausanne ; Unser, M.

Our aim in this paper is to tighten the link between wavelets, some classical image-processing operators, and David Marr's theory of early vision. The cornerstone of our approach is a new complex wavelet basis that behaves like a smoothed version of the Gradient-Laplace operator. Starting from first principles, we show that a single-generator wavelet can be defined analytically and that it yields a semi-orthogonal complex basis of L 2(R2), irrespective of the dilation matrix used. We also provide an efficient FFT-based filterbank implementation. We then propose a slightly redundant version of the transform that is nearly translation-invariant and that is optimized for better steerability (Gaussian-like smoothing kernel). We call it the Marr-like wavelet pyramid because it essentially replicates the processing steps in Marr's theory of early vision. We use it to derive a primal wavelet sketch which is a compact description of the image by a multiscale, subsampled edge map. Finally, we provide an efficient iterative algorithm for the reconstruction of an image from its primal wavelet sketch.

Published in:

Image Processing, IEEE Transactions on  (Volume:17 ,  Issue: 11 )

Date of Publication:

Nov. 2008

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