Complex Wavelet Bases, Steerability, and the Marr-Like Pyramid
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 (R-2), 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.
- URL: http://bigwww.epfl.ch/publications/vandeville0803.html
- URL: http://bigwww.epfl.ch/publications/vandeville0803.ps
Keywords: Feature extraction ; primal sketch ; steerable filters ; wavelet design ; Zero-Crossings ; Edge-Detection ; Image-Analysis ; Scale-Space ; Transform ; Signals ; Design ; Reconstruction ; Representation ; Decomposition ; CIBM-SP
Record created on 2010-11-30, modified on 2016-08-09