OTB  9.0.0 Orfeo Toolbox
Markov/MarkovRestorationExample.cxx
/*
* Copyright (C) 2005-2022 Centre National d'Etudes Spatiales (CNES)
*
* This file is part of Orfeo Toolbox
*
* https://www.orfeo-toolbox.org/
*
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
*
* Unless required by applicable law or agreed to in writing, software
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
*/
/* Example usage:
./MarkovRestorationExample Input/QB_Suburb.png Input/QB_Suburb.png Output/MarkovRestoration.png 10.0 30 1.0 1
*/
// The Markov Random Field framework can be used to apply an edge preserving
// filtering, thus playing a role of restoration.
//
// This example applies the \doxygen{otb}{MarkovRandomFieldFilter} for
// image restoration. The structure of the example is similar to the other MRF example.
// The original image is assumed to be coded in one byte, thus 256 states
// are possible for each pixel. The only other modifications reside in the energy
// function chosen for the fidelity and for the regularization.
//
// For the regularization energy function, we choose an edge preserving function:
//
//
// \Phi(u) = \frac{u^2}{1+u^2}
//
//
// and for the fidelity function, we choose a gaussian model.
//
// The starting state of the Markov Random Field is given by the image itself
// as the final state should not be too far from it.
#include "otbImage.h"
#include "itkUnaryFunctorImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
// The first step toward the use of this filter is the inclusion of the proper
int main(int argc, char* argv[])
{
if (argc != 8)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " inputImage inputInitialization output lambda iterations optimizerTemperature" << std::endl;
std::cerr << " useRandomValue" << std::endl;
return 1;
}
// We declare the usual types:
const unsigned int Dimension = 2;
using InternalPixelType = double;
using LabelledPixelType = unsigned char;
using LabelledImageType = otb::Image<LabelledPixelType, Dimension>;
// We need to declare an additional reader for the initial state of the
// MRF. This reader has to be instantiated on the LabelledImageType.
WriterType::Pointer writer = WriterType::New();
const char* inputFilename = argv[1];
const char* labelledFilename = argv[2];
const char* outputFilename = argv[3];
writer->SetFileName(outputFilename);
// We declare all the necessary types for the MRF:
using OptimizerType = otb::MRFOptimizerMetropolis;
// The regularization and the fidelity energy are declared and instantiated:
MarkovRandomFieldFilterType::Pointer markovFilter = MarkovRandomFieldFilterType::New();
EnergyRegularizationType::Pointer energyRegularization = EnergyRegularizationType::New();
EnergyFidelityType::Pointer energyFidelity = EnergyFidelityType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
SamplerType::Pointer sampler = SamplerType::New();
if ((bool)(atoi(argv[7])) == true)
{
// Overpass random calculation(for test only):
sampler->InitializeSeed(0);
optimizer->InitializeSeed(1);
markovFilter->InitializeSeed(2);
}
// The number of possible states for each pixel is 256 as the image is assumed
// to be coded on one byte and we pass the parameters to the markovFilter.
unsigned int nClass = 256;
optimizer->SetSingleParameter(atof(argv[6]));
markovFilter->SetNumberOfClasses(nClass);
markovFilter->SetMaximumNumberOfIterations(atoi(argv[5]));
markovFilter->SetErrorTolerance(0.0);
markovFilter->SetLambda(atof(argv[4]));
markovFilter->SetEnergyRegularization(energyRegularization);
markovFilter->SetEnergyFidelity(energyFidelity);
markovFilter->SetOptimizer(optimizer);
markovFilter->SetSampler(sampler);
// The original state of the MRF filter is passed through the
// \code{SetTrainingInput()} method:
// And we plug the pipeline:
using RescaleType = itk::RescaleIntensityImageFilter<LabelledImageType, LabelledImageType>;
RescaleType::Pointer rescaleFilter = RescaleType::New();
rescaleFilter->SetOutputMinimum(0);
rescaleFilter->SetOutputMaximum(255);
rescaleFilter->SetInput(markovFilter->GetOutput());
writer->SetInput(rescaleFilter->GetOutput());
try
{
writer->Update();
}
catch (itk::ExceptionObject& err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return -1;
}
// Figure~\ref{fig:MRF_RESTORATION} shows the output of the Markov Random
// Field restoration.
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{QB_Suburb.eps}
// \includegraphics[width=0.44\textwidth]{MarkovRestoration.eps}
// \itkcaption[MRF restoration]{Result of applying
// the \doxygen{otb}{MarkovRandomFieldFilter} to an extract from a PAN Quickbird
// image for restoration. From left to right : original image, restaured image
// with edge preservation.}
// \label{fig:MRF_RESTORATION}
// \end{figure}
return EXIT_SUCCESS;
}
otbImage.h
otb::MRFOptimizerMetropolis
This is the optimizer class implementing the Metropolis algorithm.
Definition: otbMRFOptimizerMetropolis.h:51
otb::Image
Creation of an "otb" image which contains metadata.
Definition: otbImage.h:89
otbMRFEnergyEdgeFidelity.h
otbMarkovRandomFieldFilter.h
otb::MRFEnergyEdgeFidelity
This is the implementation of an edge preserving model for Markov denoising.
Definition: otbMRFEnergyEdgeFidelity.h:51
main
int main(int ac, char *av[])
Definition: otbTestMain.h:88
otbMRFEnergyGaussian.h
otbMRFSamplerRandom.h
otbMRFOptimizerMetropolis.h
otb::MRFEnergyGaussian
This is the implementation of the Gaussian model for Markov classification.
Definition: otbMRFEnergyGaussian.h:52
otb::ImageFileWriter
Writes image data to a single file with streaming process.
Definition: otbImageFileWriter.h:66
otbImageFileWriter.h
otb::MarkovRandomFieldFilter
This is the class to use the Markov Random Field framework in OTB.
Definition: otbMarkovRandomFieldFilter.h:86