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

versión (4.39 KB) por Damien Garcia
Y = INPAINTN(X) computes the missing data in the N-D array X.

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Actualizada 20 Jun 2020

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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:
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);

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

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.

A series of examples is available here:

%% ---- 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')


Citar como

Damien Garcia (2022). Inpaint over missing data in 1-D, 2-D, 3-D,... ND arrays (, MATLAB Central File Exchange. Recuperado .

Wang, Guojie, et al. “A Three-Dimensional Gap Filling Method for Large Geophysical Datasets: Application to Global Satellite Soil Moisture Observations.” Environmental Modelling & Software, vol. 30, Elsevier BV, Apr. 2012, pp. 139–42, doi:10.1016/j.envsoft.2011.10.015.

Ver más estilos

Garcia, Damien. “Robust Smoothing of Gridded Data in One and Higher Dimensions with Missing Values.” Computational Statistics & Data Analysis, vol. 54, no. 4, Elsevier BV, Apr. 2010, pp. 1167–78, doi:10.1016/j.csda.2009.09.020.

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