文件列表:

adapting a wavelet pyramid to natural images\IMAGES.mat (20971720, 2007-07-13)
adapting a wavelet pyramid to natural images\matlabPyrTools\binomialFilter.m (309, 1997-04-27)
adapting a wavelet pyramid to natural images\matlabPyrTools\blurDn.m (1215, 1999-03-26)
adapting a wavelet pyramid to natural images\matlabPyrTools\buildGpyr.m (1931, 1998-04-17)
adapting a wavelet pyramid to natural images\matlabPyrTools\buildLpyr.m (2632, 1998-04-17)
adapting a wavelet pyramid to natural images\matlabPyrTools\buildSFpyr.m (3260, 2000-09-05)
adapting a wavelet pyramid to natural images\matlabPyrTools\buildSFpyrLevs.m (1889, 2001-03-24)
adapting a wavelet pyramid to natural images\matlabPyrTools\buildSpyr.m (2017, 1998-04-17)
adapting a wavelet pyramid to natural images\matlabPyrTools\buildSpyrLevs.m (861, 1997-05-02)
adapting a wavelet pyramid to natural images\matlabPyrTools\buildWpyr.m (2605, 1998-04-17)
adapting a wavelet pyramid to natural images\matlabPyrTools\cconv2.m (1325, 1997-04-27)
adapting a wavelet pyramid to natural images\matlabPyrTools\ChangeLog (13784, 2001-03-28)
adapting a wavelet pyramid to natural images\matlabPyrTools\Contents.m (5359, 2001-03-29)
adapting a wavelet pyramid to natural images\matlabPyrTools\corrDn.m (2241, 2001-03-28)
adapting a wavelet pyramid to natural images\matlabPyrTools\corrDn.mex4 (140703, 1998-04-27)
adapting a wavelet pyramid to natural images\matlabPyrTools\corrDn.mexglx (26678, 2000-12-22)
adapting a wavelet pyramid to natural images\matlabPyrTools\corrDn.mexlx (22398, 1998-04-27)
adapting a wavelet pyramid to natural images\matlabPyrTools\corrDn.mexsol (29556, 1998-04-27)
adapting a wavelet pyramid to natural images\matlabPyrTools\einstein.pgm (65596, 1997-04-29)
adapting a wavelet pyramid to natural images\matlabPyrTools\entropy2.m (579, 1997-04-27)
adapting a wavelet pyramid to natural images\matlabPyrTools\factorial.m (120, 1997-05-06)
adapting a wavelet pyramid to natural images\matlabPyrTools\histo.m (1835, 2001-03-28)
adapting a wavelet pyramid to natural images\matlabPyrTools\histo.mex4 (14698, 1998-04-27)
adapting a wavelet pyramid to natural images\matlabPyrTools\histo.mexglx (8934, 2000-12-22)
adapting a wavelet pyramid to natural images\matlabPyrTools\histo.mexlx (6401, 1998-04-27)
adapting a wavelet pyramid to natural images\matlabPyrTools\histo.mexsol (7304, 1998-04-27)
adapting a wavelet pyramid to natural images\matlabPyrTools\histoMatch.m (858, 1998-05-06)
adapting a wavelet pyramid to natural images\matlabPyrTools\ifftshift.m (407, 1997-04-27)
adapting a wavelet pyramid to natural images\matlabPyrTools\imStats.m (1105, 1997-08-22)
adapting a wavelet pyramid to natural images\matlabPyrTools\innerProd.m (404, 2001-03-28)
adapting a wavelet pyramid to natural images\matlabPyrTools\innerProd.mexglx (6751, 2000-12-22)
adapting a wavelet pyramid to natural images\matlabPyrTools\innerProd.mexlx (4479, 1998-05-06)
adapting a wavelet pyramid to natural images\matlabPyrTools\innerProd.mexsol (3948, 1997-08-19)
adapting a wavelet pyramid to natural images\matlabPyrTools\kurt2.m (551, 1997-08-22)
adapting a wavelet pyramid to natural images\matlabPyrTools\lplot.m (1008, 1997-08-30)
adapting a wavelet pyramid to natural images\matlabPyrTools\lpyrHt.m (233, 1997-04-27)
adapting a wavelet pyramid to natural images\matlabPyrTools\maxPyrHt.m (603, 1997-04-27)
adapting a wavelet pyramid to natural images\matlabPyrTools\mean2.m (97, 1997-04-27)
adapting a wavelet pyramid to natural images\matlabPyrTools\MEX\convolve.c (10523, 1997-09-07)
adapting a wavelet pyramid to natural images\matlabPyrTools\MEX\convolve.h (1863, 1997-09-07)
... ...

This directory contains matlab scripts and subroutines for learning a wavelet basis from natural scenes. These should enable you to duplicate the results described in our paper, "Learning sparse image codes using a wavelet pyramid architecture," published in vol. 12 of the NIPS conference proceedings (available via ftp://redwood.ucdavis.edu/ pub/papers/nips00.ps.gz). These routines are built upon Eero Simoncelli's matlab pyramid toolbox, so you will need to have that installed somewhere in your path in order to run the program. You may obtain this toolbox from Eero's web page at http://www.cns.nyu.edu/~eero. You may find it helpful to first go through Eero's tutorial (in the TUTORIALS sub- directory), as many of the array structures we use are based on his conventions. The main script for running the learning algorithm is sparsepyr.m. You can run it once you start up matlab by simply typing 'sparsepyr'. You can initialize a wavelet basis in advance if you like, using randfilt, or if not one will be generated for you. Read the comments in sparsepyr.m for more information. The files f54.mat and f55.mat contain 5x5 filters, 3 and 4 bands (not including scaling function) respectively, that were learned with the current parameter settings in sparsepyr.m. You can look at these with the functions showbasis, showbasisfft, and showtiling. The main parameters you should play with to see how it affects the result are the learning rate (eta) and the noise variance (1/lambda_N). Note that changing beta is tantamount to changing lambda_N, so I usually leave beta fixed at 2.5 and play with lambda_N. You probably don't want to change the scale parameter sigma unless you also change VAR_GOAL, and you shouldn't do this unless you work with data that has a different variance (in which case you should also change lambda_N appropriately). If you want to use your own image set, you should first preprocess them by whitening/lowpass filtering. I did this by multiplying by the following filter in the frequency domain: R(f) = f.*exp(-(f/f_0).^4); where f is the radius in the 2D spatial-frequency plane, and f_0 is 0.8*f_N, where f_N is the Nyquist frequency. Without whitening, the sparsification step will require many more iterations (because the curvature will be extremely high along the high spatial-frequency axes and low along the low spatial-frequency axes), and without the lowpass filter the bases are bound to come out looking different for horizontal/vertical vs. diagonal orientations due to the rectangular sampling lattice. Questions to: Bruno Olshausen (baolshausen@ucdavis.edu) or Phil Sallee (sallee@cs.ucdavis.edu)

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