diff options
Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m | 6 | ||||
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_denoise.m | 6 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/regularisers_CPU/TV_energy.c | 9 |
3 files changed, 11 insertions, 10 deletions
diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m index 84889d7..5a54d18 100644 --- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m @@ -21,7 +21,7 @@ fprintf('Denoise a volume using the ROF-TV model (CPU) \n'); tau_rof = 0.0025; % time-marching constant iter_rof = 300; % number of ROF iterations tic; u_rof = ROF_TV(single(vol3D), lambda_reg, iter_rof, tau_rof); toc; -energyfunc_val_rof = TV_energy(single(u_rof),single(vol3D),lambda_reg); % get energy function value +energyfunc_val_rof = TV_energy(single(u_rof),single(vol3D),lambda_reg, 1); % get energy function value figure; imshow(u_rof(:,:,15), [0 1]); title('ROF-TV denoised volume (CPU)'); %% % fprintf('Denoise a volume using the ROF-TV model (GPU) \n'); @@ -34,7 +34,7 @@ fprintf('Denoise a volume using the FGP-TV model (CPU) \n'); iter_fgp = 300; % number of FGP iterations epsil_tol = 1.0e-05; % tolerance tic; u_fgp = FGP_TV(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; -energyfunc_val_fgp = TV_energy(single(u_fgp),single(vol3D),lambda_reg); % get energy function value +energyfunc_val_fgp = TV_energy(single(u_fgp),single(vol3D),lambda_reg, 1); % get energy function value figure; imshow(u_fgp(:,:,15), [0 1]); title('FGP-TV denoised volume (CPU)'); %% % fprintf('Denoise a volume using the FGP-TV model (GPU) \n'); @@ -47,7 +47,7 @@ fprintf('Denoise a volume using the SB-TV model (CPU) \n'); iter_sb = 150; % number of SB iterations epsil_tol = 1.0e-05; % tolerance tic; u_sb = SB_TV(single(vol3D), lambda_reg, iter_sb, epsil_tol); toc; -energyfunc_val_sb = TV_energy(single(u_sb),single(vol3D),lambda_reg); % get energy function value +energyfunc_val_sb = TV_energy(single(u_sb),single(vol3D),lambda_reg, 1); % get energy function value figure; imshow(u_sb(:,:,15), [0 1]); title('SB-TV denoised volume (CPU)'); %% % fprintf('Denoise a volume using the SB-TV model (GPU) \n'); diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m index 526d21c..151a604 100644 --- a/Wrappers/Matlab/demos/demoMatlab_denoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m @@ -14,7 +14,7 @@ fprintf('Denoise using the ROF-TV model (CPU) \n'); tau_rof = 0.0025; % time-marching constant iter_rof = 750; % number of ROF iterations tic; u_rof = ROF_TV(single(u0), lambda_reg, iter_rof, tau_rof); toc; -energyfunc_val_rof = TV_energy(single(u_rof),single(u0),lambda_reg); % get energy function value +energyfunc_val_rof = TV_energy(single(u_rof),single(u0),lambda_reg, 1); % get energy function value figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)'); %% % fprintf('Denoise using the ROF-TV model (GPU) \n'); @@ -27,7 +27,7 @@ fprintf('Denoise using the FGP-TV model (CPU) \n'); iter_fgp = 1000; % number of FGP iterations epsil_tol = 1.0e-06; % tolerance tic; u_fgp = FGP_TV(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; -energyfunc_val_fgp = TV_energy(single(u_fgp),single(u0),lambda_reg); % get energy function value +energyfunc_val_fgp = TV_energy(single(u_fgp),single(u0),lambda_reg, 1); % get energy function value figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)'); %% @@ -41,7 +41,7 @@ fprintf('Denoise using the SB-TV model (CPU) \n'); iter_sb = 150; % number of SB iterations epsil_tol = 1.0e-06; % tolerance tic; u_sb = SB_TV(single(u0), lambda_reg, iter_sb, epsil_tol); toc; -energyfunc_val_sb = TV_energy(single(u_sb),single(u0),lambda_reg); % get energy function value +energyfunc_val_sb = TV_energy(single(u_sb),single(u0),lambda_reg, 1); % get energy function value figure; imshow(u_sb, [0 1]); title('SB-TV denoised image (CPU)'); %% % fprintf('Denoise using the SB-TV model (GPU) \n'); diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/TV_energy.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/TV_energy.c index 421bd4c..f9eb2ce 100644 --- a/Wrappers/Matlab/mex_compile/regularisers_CPU/TV_energy.c +++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/TV_energy.c @@ -36,7 +36,7 @@ void mexFunction( int nrhs, const mxArray *prhs[]) { - int number_of_dims, dimX, dimY, dimZ; + int number_of_dims, dimX, dimY, dimZ, type; const int *dim_array; float *Input, *Input0, lambda; @@ -44,11 +44,12 @@ void mexFunction( dim_array = mxGetDimensions(prhs[0]); /*Handling Matlab input data*/ - if ((nrhs != 3)) mexErrMsgTxt("3 inputs: Two images or volumes of the same size required, estimated and the original (noisy), regularisation parameter"); + if ((nrhs != 4)) mexErrMsgTxt("4 inputs: Two images or volumes of the same size required, estimated and the original (noisy), regularisation parameter, type"); Input = (float *) mxGetData(prhs[0]); /* Denoised Image/volume */ Input0 = (float *) mxGetData(prhs[1]); /* Original (noisy) Image/volume */ lambda = (float) mxGetScalar(prhs[2]); /* regularisation parameter */ + type = (int) mxGetScalar(prhs[3]); /* type of energy */ if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); } if (mxGetClassID(prhs[1]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); } @@ -61,9 +62,9 @@ void mexFunction( dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2]; if (number_of_dims == 2) { - TV_energy2D(Input, Input0, funcvalA, lambda, dimX, dimY); + TV_energy2D(Input, Input0, funcvalA, lambda, type, dimX, dimY); } if (number_of_dims == 3) { - TV_energy3D(Input, Input0, funcvalA, lambda, dimX, dimY, dimZ); + TV_energy3D(Input, Input0, funcvalA, lambda, type, dimX, dimY, dimZ); } } |