# Matlab Code = Full variational image denoising method (total variation-based image denoising)

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Author: User
Matlab Code = Full variational image denoising method (total variation-based image denoising)Category: Total variation image denoising programming Code 2012-04-05 13:19 1196 people read reviews (0) Favorites report Image matlab reference Algor Ithm Input website

Reference

 L. Rudin, S. Osher, E. Fatemi, ' nonlinear total variation based noise removal algorithm ', Physica D 60 259-268, 1992.

related Website

 Total variation denoising:http://visl.technion.ac.il/~gilboa/pde-filt/tv_denoising.html

function J=tv (i,iter,dt,ep,lam,i0,c)

Percent Private FUNCTION:TV (by Guy Gilboa).

Percent total variation denoising.

EXAMPLE:J=TV (I,ITER,DT,EP,LAM,I0)

Percent INPUT:I-image (double array gray level 1-256),

Iter-num of iterations,

Percent Dt-time step [0.2],

Ep-epsilon (of gradient regularization) ,

Percent lam-fidelity term lambda ,

Percent I0-input (noisy) image [I0=i]

Percent (default values is in [])

Percent output:evolved image

If ~exist (' EP ')

Ep=1;

End

If ~exist (' DT ')

DT=EP/5; % DT below the CFL bound

End

If ~exist (' Lam ')

lam=0;

End

If ~exist (' I0 ')

I0=i;

End

If ~exist (' C ')

c=0;

End

[Ny,nx]=size (I); ep2=ep^2;

For i=1:iter, percent do iterations

% estimate derivatives

i_x = (I (:, [2:nx NX])-I (:, [1 1:nx-1]))/2;

I_y = (I ([2:ny NY],:)-I ([1 1:ny-1],:))/2;

I_xx = I (:, [2:nx NX]) +i (:, [1 1:nx-1]) -2*i;

I_yy = I ([2:ny NY],:) +i ([1 1:ny-1],:) -2*i;

Dp = I ([2:ny ny],[2:nx NX]) +i ([1 1:ny-1],[1 1:nx-1]);

Dm = I ([1 1:ny-1],[2:nx NX]) +i ([2:ny ny],[1 1:nx-1]);

I_xy = (DP-DM)/4;

% Compute Flow

Num = i_xx.* (ep2+i_y.^2) -2*i_x.*i_y.*i_xy+i_yy.* (ep2+i_x.^2);

Den = (ep2+i_x.^2+i_y.^2). ^ (3/2);

i_t = Num./den + lam.* (i0-i+c);

i=i+dt*i_t; Percent evolve image by DT

End% for I

Percent return image

%j=i*imean/mean (Mean (I)); % normalize to original mean

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