l2_inpainting.cc File Reference

#include <algorithm>
#include <exception>
#include <functional>
#include <iostream>
#include <random>
#include <vector>
#include <ctime>
#include "sopt/l2_forward_backward.h"
#include "sopt/logging.h"
#include "sopt/maths.h"
#include "sopt/relative_variation.h"
#include "sopt/sampling.h"
#include "sopt/types.h"
#include "sopt/utilities.h"
#include "sopt/wavelets.h"
#include "tools_for_tests/directories.h"
#include "tools_for_tests/tiffwrappers.h"

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Functions
int	main (int argc, char const **argv)

Function Documentation

main()

int main	(	int	argc,
		char const **	argv
	)

Definition at line 23 of file l2_inpainting.cc.

                                      {
  // Some type aliases for simplicity
  using Scalar = double;
  // Column vector - linear algebra - A * x is a matrix-vector multiplication
  // type expected by Forward Backward
  using Vector = sopt::Vector<Scalar>;
  // Matrix - linear algebra - A * x is a matrix-vector multiplication
  // type expected by Forward Backward
  using Matrix = sopt::Matrix<Scalar>;
  // Image - 2D array - A * x is a coefficient-wise multiplication
  // Type expected by wavelets and image write/read functions
  using Image = sopt::Image<Scalar>;
 
  std::string const input = argc >= 2 ? argv[1] : "cameraman256";
  std::string const output = argc == 3 ? argv[2] : "none";
  if (argc > 3) {
    std::cout << "Usage:\n"
                 "$ "
              << argv[0]
              << " [input [output]]\n\n"
                 "- input: path to the image to clean (or name of standard SOPT image)\n"
                 "- output: filename pattern for output image\n";
    exit(0);
  }
  // Set up random numbers for C and C++
  auto const seed = std::time(nullptr);
  std::srand(static_cast<unsigned int>(seed));
  std::mt19937 mersenne(std::time(nullptr));
 
  // See set_level function for levels.
  sopt::logging::set_level("debug");
  SOPT_HIGH_LOG("Read input file {}", input);
  Image const image = sopt::tools::read_standard_tiff(input);
  SOPT_HIGH_LOG("Image size: {} x {} = {}", image.cols(), image.rows(), image.size());
 
  SOPT_HIGH_LOG("Initializing sensing operator");
  sopt::t_uint const nmeasure = std::floor(0.5 * image.size());
  sopt::LinearTransform<Vector> const sampling =
      sopt::linear_transform<Scalar>(sopt::Sampling(image.size(), nmeasure, mersenne));
  SOPT_HIGH_LOG("Initializing wavelets");
  auto const wavelet = sopt::wavelets::factory("DB8", 4);
 
  // sopt::wavelets::SARA const wavelet{std::make_tuple("db1", 4u), std::make_tuple("db2", 4u),
  //                                    std::make_tuple("db3", 4u), std::make_tuple("db4", 4u)};
 
  auto const psi = sopt::linear_transform<Scalar>(wavelet, image.rows(), image.cols());
  SOPT_LOW_LOG("Wavelet coefficients: {}", (psi.adjoint() * image).size());
 
  SOPT_HIGH_LOG("Computing Forward Backward parameters");
  Vector const y0 = sampling * Vector::Map(image.data(), image.size());
  auto constexpr snr = 30.0;
  auto const sigma = y0.stableNorm() / std::sqrt(y0.size()) * std::pow(10.0, -(snr / 20.0));
  auto const epsilon = std::sqrt(nmeasure + 2 * std::sqrt(y0.size())) * sigma;
 
  SOPT_HIGH_LOG("Create dirty vector");
  std::normal_distribution<> gaussian_dist(0, sigma);
  Vector y(y0.size());
  for (sopt::t_int i = 0; i < y0.size(); i++) y(i) = y0(i) + gaussian_dist(mersenne);
  // Write dirty imagte to file
  if (output != "none") {
    Vector const dirty = sampling.adjoint() * y;
    sopt::utilities::write_tiff(Matrix::Map(dirty.data(), image.rows(), image.cols()),
                                "dirty_" + output + ".tiff");
  }
  constexpr sopt::t_real x_sigma = 1.;
  sopt::t_real constexpr regulariser_strength = 1. / (x_sigma * x_sigma * 2);
  sopt::t_real const step_size = sigma * sigma * 0.5;
  SOPT_HIGH_LOG("Creating Foward Backward Functor");
  auto const fb = sopt::algorithm::L2ForwardBackward<Scalar>(y)
                      .itermax(500)
                      .step_size(step_size)    // stepsize
                      .sigma(sigma)  // sigma
                      .regulariser_strength(regulariser_strength)  // regularisation paramater
                      .relative_variation(1e-3)
                      .residual_tolerance(0)
                      .tight_frame(true)
                      .Phi(sampling);
 
  SOPT_HIGH_LOG("Starting Forward Backward");
  // Alternatively, forward-backward can be called with a tuple (x, residual) as argument
  // Here, we default to (y, Φx/ν - y)
  Vector const init_map = Vector::Ones(image.size()) * x_sigma;
  Vector const init_res = y - (sampling * init_map);
  const std::tuple<Vector, Vector> warm_start = {init_map, init_res};
  auto const diagnostic = fb();
  SOPT_HIGH_LOG("Forward backward returned {}", diagnostic.good);
 
  if (output != "none")
    sopt::utilities::write_tiff(Matrix::Map(diagnostic.x.data(), image.rows(), image.cols()),
                                output + ".tiff");
  // diagnostic should tell us the function converged
  // it also contains diagnostic.niters - the number of iterations, and cg_diagnostic - the
  // diagnostic from the last call to the conjugate gradient.
  if (not diagnostic.good) throw std::runtime_error("Did not converge!");
 
  SOPT_HIGH_LOG("SOPT-Forward Backward converged in {} iterations", diagnostic.niters);
 
  return 0;
}

References sopt::LinearTransform< VECTOR >::adjoint(), sopt::dirty(), sopt::epsilon(), sopt::wavelets::factory(), mersenne(), sopt::algorithm::L2ForwardBackward< SCALAR >::Phi(), sopt::tools::read_standard_tiff(), sopt::logging::set_level(), sopt::sigma(), SOPT_HIGH_LOG, SOPT_LOW_LOG, and sopt::utilities::write_tiff().