📄 equalize.m
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function out=equalize(in,levels)%Function out=equalize(in,levels) performs histogram equalization on %the vector passed by 'in' %%Inputs: in - An Nx1 vector of N values% levels - The granularity of the historgram. % If not defined, defaults to 256%%Outputs: out - A vector of N, equalized, values with the same range% as the input values%%Note: Histogram is equalized across each dimension independently%%%5/21/03 - Leo Grady% Copyright (C) 2002, 2003 Leo Grady <lgrady@cns.bu.edu>% Computer Vision and Computational Neuroscience Lab% Department of Cognitive and Neural Systems% Boston University% Boston, MA 02215%% This program is free software; you can redistribute it and/or% modify it under the terms of the GNU General Public License% as published by the Free Software Foundation; either version 2% of the License, or (at your option) any later version.%% This program is distributed in the hope that it will be useful,% but WITHOUT ANY WARRANTY; without even the implied warranty of% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the% GNU General Public License for more details.%% You should have received a copy of the GNU General Public License% along with this program; if not, write to the Free Software% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.%% Date - $Id: equalize.m,v 1.3 2003/08/21 17:29:29 lgrady Exp $%========================================================================%%Read inputsif nargin < 2 levels=256;end%Parameters[N P]=size(in);%Error check for single inputif N == 1 out=in; returnend%Normalize inputs to the interval [0,1]minOrig=min(in);maxOrig=max(in);normVals=normalize(in);%Find normalized histograminHist=hist(in,levels)./N;%Cumulative probability distributioninCDF=cumsum(inHist)';%Transform input to equalized inputtmp=1+round(normVals.*(levels-1));out=inCDF(tmp);%Normalize equalized outputout=normalize(out,[minOrig;maxOrig]);
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