Berkeley Wavelet Transform

Functions performing the Berkeley Wavelet Transform and its inverse

Description

The Berkeley Wavelet Transform (BWT) comprises four pairs of mother wavelets at four orientations. Within each pair, one wavelet has odd symmetry, and the other has even symmetry. By translation and scaling of the whole set (plus a single constant term), the wavelets form a complete, orthonormal basis in two dimensions.  
 
The BWT shares many characteristics with the receptive fields of neurons in mammalian primary visual cortex (V1). Like these receptive fields, BWT wavelets are localized in space, tuned in spatial frequency and orientation, and form a set that is approximately scale invariant. The wavelets also have spatial frequency and orientation bandwidths that are comparable with biological values.  
 
Although the classical Gabor wavelet model is a more accurate description of the receptive fields of individual V1 neurons, the BWT has some interesting advantages. It is a complete, orthonormal basis and is therefore inexpensive to compute, manipulate, and invert. These properties make the BWT useful in situations where computational power or experimental data are limited, such as estimation of the spatiotemporal receptive fields of neurons.  
 
See <http://dx.doi.org/10.1162/neco.2007.05-07-513> for more details, or:  
Willmore B, Prenger RJ, Wu MC and Gallant JL (2008a). The Berkeley Wavelet Transform: A biologically-inspired orthogonal wavelet transform. Neural Computation 20:6, 1537–1564

• MATLAB release: MATLAB 7.6 (R2008a)
• Other Files: bwt/bwt.m, ; bwt/bwt_onelevel.m, ; bwt/bwt_test.m, ; bwt/bwtfilter.m, ; bwt/bwtrecon.m, ; bwt/ibwt.m, ; bwt/ibwt_onelevel.m, ; bwt/makebw.m

[Ben Hong]

It's interesting and useful. I like novel ideas and techniques!

[bigforg ‡]