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diff --git a/SD-VBS/benchmarks/texture_synthesis/src/matlab/modkurt.m b/SD-VBS/benchmarks/texture_synthesis/src/matlab/modkurt.m
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index e144d32..0000000
--- a/SD-VBS/benchmarks/texture_synthesis/src/matlab/modkurt.m
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-function [chm, snrk] = modkurt(ch,k,p);
-
-% Modify the kurtosis in one step, by moving in gradient direction until
-% reaching the desired kurtosis value. 
-% It does not affect the mean nor the variance, but it affects the skewness.
-% This operation is not an orthogonal projection, but the projection angle is
-% near pi/2 when k is close to the original kurtosis, which is a realistic assumption
-% when doing iterative projections in a pyramid, for example (small corrections
-% to the channels' statistics). 
-%
-% [chm, snrk] = modkurt(ch,k,p);
-%       ch: channel
-%       k: desired kurtosis (k=M4/M2^2) 
-%       p [OPTIONAL]:   mixing proportion between k0 and k
-%                       it imposes (1-p)*k0 + p*k,
-%                       being k0 the current kurtosis.
-%                       DEFAULT: p = 1;
-
-
-% Javier Portilla,  Oct.12/97, NYU
-
-Warn = 0;  % Set to 1 if you want to see warning messages
-if ~exist('p'),
-  p = 1;
-end
-
-me=mean2(ch);
-ch=ch-me;
-
-% Compute the moments
-
-m=zeros(12,1);
-for n=2:12,
-        m(n)=mean2(ch.^n);
-end
-
-% The original kurtosis
-
-k0=m(4)/m(2)^2;
-snrk = snr(k, k-k0);
-if snrk > 60,
-        chm = ch+me;
-        return
-end
-k = k0*(1-p) + k*p;
-
-% Some auxiliar variables
-
-a=m(4)/m(2);
-
-% Coeficients of the numerator (A*lam^4+B*lam^3+C*lam^2+D*lam+E)
-
-A=m(12)-4*a*m(10)-4*m(3)*m(9)+6*a^2*m(8)+12*a*m(3)*m(7)+6*m(3)^2*m(6)-...
-        4*a^3*m(6)-12*a^2*m(3)*m(5)+a^4*m(4)-12*a*m(3)^2*m(4)+...
-        4*a^3*m(3)^2+6*a^2*m(3)^2*m(2)-3*m(3)^4;
-B=4*(m(10)-3*a*m(8)-3*m(3)*m(7)+3*a^2*m(6)+6*a*m(3)*m(5)+3*m(3)^2*m(4)-...
-        a^3*m(4)-3*a^2*m(3)^2-3*m(4)*m(3)^2);
-C=6*(m(8)-2*a*m(6)-2*m(3)*m(5)+a^2*m(4)+2*a*m(3)^2+m(3)^2*m(2));
-D=4*(m(6)-a^2*m(2)-m(3)^2);
-E=m(4);
-
-% Define the coefficients of the denominator (F*lam^2+G)^2
-
-F=D/4;
-G=m(2);
-
-% test
-test = 0;
-
-if test,
-
-        grd = ch.^3 - a*ch - m(3);
-        lam = -0.001:0.00001:0.001;
-        k = (A*lam.^4+B*lam.^3+C*lam.^2+D*lam+E)./...
-                (F*lam.^2 + G).^2;
-        for lam = -0.001:0.00001:0.001,
-                n = lam*100000+101;
-                chp = ch + lam*grd;
-                k2(n) = mean2(chp.^4)/mean2(chp.^2)^2;
-                %k2(n) = mean2(chp.^4);
-        end
-        lam = -0.001:0.00001:0.001;
-        snr(k2, k-k2)
-
-end % test
-
-% Now I compute its derivative with respect to lambda
-% (only the roots of derivative = 0 )
-
-d(1) = B*F;
-d(2) = 2*C*F - 4*A*G;
-d(3) = 4*F*D -3*B*G - D*F;
-d(4) = 4*F*E - 2*C*G;
-d(5) = -D*G;
-
-mMlambda = roots(d);
-
-tg = imag(mMlambda)./real(mMlambda);
-mMlambda = mMlambda(find(abs(tg)<1e-6));
-lNeg = mMlambda(find(mMlambda<0));
-if length(lNeg)==0,
-        lNeg = -1/eps;
-end
-lPos = mMlambda(find(mMlambda>=0));
-if length(lPos)==0,
-        lPos = 1/eps;
-end
-lmi = max(lNeg);
-lma = min(lPos);
-
-lam = [lmi lma];
-mMnewKt = polyval([A B C D E],lam)./(polyval([F 0 G],lam)).^2;
-kmin = min(mMnewKt);
-kmax = max(mMnewKt);
-
-% Given a desired kurtosis, solves for lambda
-
-if k<=kmin & Warn,
-        lam = lmi;
-        warning('Saturating (down) kurtosis!');
-        kmin
-elseif k>=kmax & Warn,
-        lam = lma;
-        warning('Saturating (up) kurtosis!');
-        kmax
-else
-
-% Coeficients of the algebraic equation
-
-c0 = E - k*G^2;
-c1 = D;
-c2 = C - 2*k*F*G;
-c3 = B;
-c4 = A - k*F^2;
-
-% Solves the equation
-
-r=roots([c4 c3 c2 c1 c0]);
-
-% Chose the real solution with minimum absolute value with the rigth sign
-
-tg = imag(r)./real(r);
-%lambda = real(r(find(abs(tg)<1e-6)));
-lambda = real(r(find(abs(tg)==0)));
-if length(lambda)>0,
-        lam = lambda(find(abs(lambda)==min(abs(lambda))));
-        lam = lam(1);
-else
-        lam = 0;
-end
-
-end % if ... else
-
-
-% Modify the channel
-
-chm=ch+lam*(ch.^3-a*ch-m(3));   % adjust the kurtosis
-chm=chm*sqrt(m(2)/mean2(chm.^2));       % adjust the variance
-chm=chm+me;                             % adjust the mean
-
-% Check the result
-%k2=mean2((chm-me).^4)/(mean2((chm-me).^2))^2;
-%SNR=snr(k,k-k2)
-
-
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