00001 // This is brl/bseg/sdet/sdet_denoise_mrf.h 00002 #ifndef sdet_denoise_mrf_h_ 00003 #define sdet_denoise_mrf_h_ 00004 //--------------------------------------------------------------------- 00005 //: 00006 // \file 00007 // \brief a processor applying a binary mrf denoise algorithm 00008 // 00009 // 00010 // This algorithm selectively smooths the image based on 00011 // a variance value at each pixel. The smoothing is carried out 00012 // by a MRF with binary cliques, where the clique energy weights 00013 // are controlled in part by the variance image. 00014 // 00015 // 00016 // \author 00017 // J.L. Mundy - March 22, 2011 00018 // 00019 // 00020 // The mrf energy minimization is carried out with a closed form algorithm 00021 // i.e. non-iterative as compared with belief propagation, 00022 // which requires iterations of message passing. The algorithm is based in 00023 // part on the paper: 00024 // "Continuous MRF-based image denoising with a closed form solution," 00025 // Ming Liu, Shifeng Chen, and Jianzhuang Liu 00026 // Proc. IEEE 17th International Conference on Image Processing, 2010 00027 // 00028 // 00029 // \verbatim 00030 // Modifications 00031 // <none> 00032 // \endverbatim 00033 // 00034 //------------------------------------------------------------------------- 00035 #include <vcl_vector.h> 00036 #include <vnl/vnl_vector.h> 00037 #include <sdet/sdet_denoise_mrf_params.h> 00038 #include <vil/vil_image_resource.h> 00039 #include <vil/vil_image_view.h> 00040 #include <vnl/vnl_sparse_matrix.h> 00041 class sdet_denoise_mrf : public sdet_denoise_mrf_params 00042 { 00043 public: 00044 // === Constructors/destructor === 00045 00046 sdet_denoise_mrf(sdet_denoise_mrf_params& imp); 00047 ~sdet_denoise_mrf(); 00048 00049 // === Process methods === 00050 00051 void set_image(vil_image_resource_sptr const& resource) { in_resc_ = resource; } 00052 void set_variance(vil_image_resource_sptr const& var_resc) 00053 { var_resc_ = var_resc; } 00054 00055 //: construct the mrf graph incidence matrix (no output image) 00056 // W and D are valid after construction 00057 void compute_incidence_matrix(); 00058 00059 //: the full denoising process 00060 bool denoise(); 00061 00062 // === Accessors === 00063 00064 vil_image_resource_sptr output() { return out_resc_; } 00065 bool output_valid() const { return output_valid_; } 00066 vnl_sparse_matrix<double>& incidence_matrix() { return W_mat_; } 00067 vnl_sparse_matrix<double>& diag_matrix() { return D_mat_; } 00068 vil_image_resource_sptr Dimgr(); 00069 vnl_sparse_matrix<double>& diag_inv_sqrt() { return D_inv_sqrt_; } 00070 vnl_sparse_matrix<double>& L() { return L_mat_; } 00071 vnl_vector<double>& F() { return F_; } 00072 00073 protected: 00074 // === protected methods === 00075 00076 //: compute the clique energy weight between two image locations 00077 double weight(unsigned i0, unsigned j0, unsigned i1, unsigned j1, 00078 vil_image_view<float> const& depth, 00079 vil_image_view<float> const& varv); 00080 //:the "L" matrix 00081 void compute_laplacian_matrix(); 00082 //:the solution vector 00083 void compute_F(); 00084 00085 // === members === 00086 00087 bool output_valid_; //process state flag 00088 double sigma_sq_inv_; 00089 vil_image_resource_sptr in_resc_; 00090 vil_image_resource_sptr var_resc_; 00091 vil_image_resource_sptr out_resc_; 00092 vnl_sparse_matrix<double> W_mat_; 00093 vnl_sparse_matrix<double> D_mat_; 00094 vnl_sparse_matrix<double> D_inv_sqrt_; 00095 vnl_sparse_matrix<double> L_mat_; 00096 vnl_sparse_matrix<double> inv_mat_; 00097 vnl_vector<double> F_; 00098 }; 00099 00100 #endif // sdet_denoise_mrf_h_
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