Merge remote-tracking branch 'remotes/origin/master' into newdirstructure

Conflicts:
	demos/demoMatlab_denoise.m
	demos/qualitymetrics.py

1 files changed, 17 insertions, 15 deletions

diff --git a/src/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp
index edb551d..1173282 100644
--- a/src/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp
+++ b/src/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp
@@ -21,18 +21,18 @@ limitations under the License.
 #include "TGV_GPU_core.h"
 
 /* CUDA implementation of Primal-Dual denoising method for 
- * Total Generilized Variation (TGV)-L2 model [1] (2D case only)
+ * Total Generilized Variation (TGV)-L2 model [1] (2D/3D)
  *
  * Input Parameters:
- * 1. Noisy image (2D) (required)
- * 2. lambda - regularisation parameter (required)
- * 3. parameter to control the first-order term (alpha1) (default - 1)
- * 4. parameter to control the second-order term (alpha0) (default - 0.5)
- * 5. Number of Chambolle-Pock (Primal-Dual) iterations (default is 300)
+ * 1. Noisy image/volume (2D/3D)
+ * 2. lambda - regularisation parameter
+ * 3. parameter to control the first-order term (alpha1)
+ * 4. parameter to control the second-order term (alpha0)
+ * 5. Number of Chambolle-Pock (Primal-Dual) iterations
  * 6. Lipshitz constant (default is 12)
  *
  * Output:
- * Filtered/regulariaed image 
+ * Filtered/regularised image 
  *
  * References:
  * [1] K. Bredies "Total Generalized Variation"
@@ -44,7 +44,7 @@ void mexFunction(
         
 {
     int number_of_dims, iter;
-    mwSize dimX, dimY;
+    mwSize dimX, dimY, dimZ;
     const mwSize *dim_array;
     float *Input, *Output=NULL, lambda, alpha0, alpha1, L2;
     
@@ -57,8 +57,8 @@ void mexFunction(
     Input  = (float *) mxGetData(prhs[0]); /*noisy image (2D) */
     lambda =  (float) mxGetScalar(prhs[1]); /* regularisation parameter */
     alpha1 =  1.0f; /* parameter to control the first-order term */ 
-    alpha0 =  0.5f; /* parameter to control the second-order term */
-    iter =  300; /* Iterations number */      
+    alpha0 =  2.0f; /* parameter to control the second-order term */
+    iter =  500; /* Iterations number */      
     L2 =  12.0f; /* Lipshitz constant */
     
     if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }   
@@ -68,12 +68,14 @@ void mexFunction(
     if (nrhs == 6)  L2 =  (float) mxGetScalar(prhs[5]); /* Lipshitz constant */
     
     /*Handling Matlab output data*/
-    dimX = dim_array[0]; dimY = dim_array[1];
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
     
     if (number_of_dims == 2) {
-        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
-        /* running the function */
-        TGV_GPU_main(Input, Output, lambda, alpha1, alpha0, iter, L2, dimX, dimY);        
+        dimZ = 1; /*2D case*/
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));        
     }
-    if (number_of_dims == 3) {mexErrMsgTxt("Only 2D images accepted");}       
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    
+    /* running the function */
+    TGV_GPU_main(Input, Output, lambda, alpha1, alpha0, iter, L2, dimX, dimY, dimZ);        
 }