Fit GLG and GFM models to image
The GLG model and the GFM model are both fitted to the wavelet coefficients of the 'lena' image. The fitted densities are compared to the observed distribution of coefficients
Contents
Load the image and compute the wavelet transform
X = imread('lena.png'); I = im2double( X ); dwtmode('per', 'nodisp'); [C, S] = wavedec2(I, 3, 'db4'); tree = dwt2_to_cell( C, S );
Fit GLG model
M = compute_moments2D( tree ); theta_glg = moment_estimation( M ); FitInfo_glg = GLG_CEM_wrapper( theta_glg, tree );
Running EM algorithm...
Direction 1 Direction 2 Direction 3
Level 1 Iteration: 18 Iteration: 21 Iteration: 17
Level 2 Iteration: 50 Iteration: 50 Iteration: 50
Level 3 Iteration: 50 Iteration: 50 Iteration: 50
Fit GFM model
theta_gfm = GFM_init( tree, 2, 0.01, 0.001 );
FitInfo_gfm = GFM_EM_wrapper( theta_gfm, tree, 'max_itr', 50 );
Running EM algorithm... Direction 1 Direction 2 Direction 3 Iteration: 36 Iteration: 29 Iteration: 50
Plot distributions
% Level and direction of transform l = 2; d = 2; y = tree{l+1}{d}(:); x = min(y):5e-3:max(y); n = length(y); [heights, locations] = hist(y, x); % Normalized histogram width = locations(2) - locations(1); heights = heights / (n*width); bar( locations, heights, 'hist' ) % Density from GLG model w = linspace(min(y), max(y), 200); glg = GLG_marginal_density( w, FitInfo_glg.model(l,1,d), FitInfo_glg.model(l,2,d) ); glg_h = line(w, glg, 'color', 'r'); % Density from GFM model gfm = GFM_marginal_density( w, FitInfo_gfm.model{l,1,d}, FitInfo_gfm.model{l,3,d} ); gfm_h = line(w, gfm, 'color', 'g'); % Format figure xlim( 0.5*[-1 1] ); legend( [glg_h, gfm_h], 'GLG density', 'GFM density' );
