Inpaint over missing data in 1-D, 2-D, 3-D,... ND arrays

Y = INPAINTN(X) computes the missing data in the N-D array X.

Y = INPAINTN(X) replaces the missing data in X by extra/interpolating the non-missing elements. The non finite values (NaN or Inf) in X are considered as missing data. X can be any N-D array.
Type "help inpaintn" in the Matlab command windows for several examples.
INPAINTN (no input/output argument) runs a 3-D example.

Important note:
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INPAINTN uses an iterative process that converges toward the solution. Y = INPAINTN(X,N) uses N iterations. By default, N = 100. If you estimate that INPAINTN did not totally converge, then increase N: Y = INPAINTN(X,1000);

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When using this algorithm, please refer to these 2 papers:

1) Garcia D. Robust smoothing of gridded data in one and higher dimensions with missing values.
Comput Statist Data Anal, 2010;54:1167-1178
http://www.biomecardio.com/publis/csda10.pdf

2) Wang G, Garcia D et al. A three- dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations.
Environ Modell Softw, 2012;30:139-142.
http://www.biomecardio.com/publis/envirmodellsoftw12.pdf.pdf
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A series of examples is available here:
http://www.biomecardio.com/matlab/inpaintn_doc.html

Example:
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%% ---- 2-D data ---- %%
n = 256;
y0 = peaks(n);
y = y0;
I = randperm(n^2);
y(I(1:n^2*0.5)) = NaN; % lose 1/2 of data
y(40:90,140:190) = NaN; % create a hole
z = inpaintn(y,200); % inpaint data
subplot(2,2,1:2), imagesc(y), axis equal off
title('Corrupt data')
subplot(223), imagesc(z), axis equal off
title('Recovered data ...')
subplot(224), imagesc(y0), axis equal off
title('... compared with original data')

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http://www.biomecardio.com
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