modifications in pdtv

4 files changed, 20 insertions, 14 deletions

diff --git a/src/Core/regularisers_CPU/PD_TV_core.c b/src/Core/regularisers_CPU/PD_TV_core.c
index cdce71b..65b8711 100644
--- a/src/Core/regularisers_CPU/PD_TV_core.c
+++ b/src/Core/regularisers_CPU/PD_TV_core.c
@@ -29,6 +29,7 @@
  * 5. lipschitz_const: convergence related parameter
  * 6. TV-type: methodTV - 'iso' (0) or 'l1' (1)
  * 7. nonneg: 'nonnegativity (0 is OFF by default, 1 is ON)
+ * 8. tau: time marching parameter
 
  * Output:
  * [1] TV - Filtered/regularized image/volume
@@ -37,17 +38,16 @@
  * [1] Antonin Chambolle, Thomas Pock. "A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging", 2010
  */
 
-float PDTV_CPU_main(float *Input, float *U, float *infovector, float lambdaPar, int iterationsNumb, float epsil, float lipschitz_const, int methodTV, int nonneg, int dimX, int dimY, int dimZ)
+float PDTV_CPU_main(float *Input, float *U, float *infovector, float lambdaPar, int iterationsNumb, float epsil, float lipschitz_const, int methodTV, int nonneg, float tau, int dimX, int dimY, int dimZ)
 {
     int ll;
     long j, DimTotal;
-    float re, re1, tau, sigma, theta, lt;
+    float re, re1, sigma, theta, lt;
     re = 0.0f; re1 = 0.0f;
     int count = 0;
 
     //tau = 1.0/powf(lipschitz_const,0.5);
     //sigma = 1.0/powf(lipschitz_const,0.5);
-    tau = 0.02;
     sigma = 1.0/(lipschitz_const*tau);
     theta = 1.0f;
     lt = tau/lambdaPar;
diff --git a/src/Core/regularisers_CPU/PD_TV_core.h b/src/Core/regularisers_CPU/PD_TV_core.h
index b4e8a75..97edc05 100644
--- a/src/Core/regularisers_CPU/PD_TV_core.h
+++ b/src/Core/regularisers_CPU/PD_TV_core.h
@@ -47,7 +47,7 @@ limitations under the License.
 #ifdef __cplusplus
 extern "C" {
 #endif
-CCPI_EXPORT float PDTV_CPU_main(float *Input, float *U, float *infovector, float lambdaPar, int iterationsNumb, float epsil, float lipschitz_const, int methodTV, int nonneg, int dimX, int dimY, int dimZ);
+float PDTV_CPU_main(float *Input, float *U, float *infovector, float lambdaPar, int iterationsNumb, float epsil, float lipschitz_const, int methodTV, int nonneg, float tau, int dimX, int dimY, int dimZ);
 
 CCPI_EXPORT float DualP2D(float *U, float *P1, float *P2, long dimX, long dimY, float sigma);
 CCPI_EXPORT float DivProj2D(float *U, float *Input, float *P1, float *P2, long dimX, long dimY, float lt, float tau);
diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py
index d65c0b9..bc745fe 100644
--- a/src/Python/ccpi/filters/regularisers.py
+++ b/src/Python/ccpi/filters/regularisers.py
@@ -53,7 +53,7 @@ def FGP_TV(inputData, regularisation_parameter,iterations,
                          .format(device))
 
 def PD_TV(inputData, regularisation_parameter, iterations,
-                     tolerance_param, methodTV, nonneg, lipschitz_const, device='cpu'):
+                     tolerance_param, methodTV, nonneg, lipschitz_const, tau, device='cpu'):
     if device == 'cpu':
         return TV_PD_CPU(inputData,
                      regularisation_parameter,
@@ -61,7 +61,8 @@ def PD_TV(inputData, regularisation_parameter, iterations,
                      tolerance_param,
                      methodTV,
                      nonneg,
-                     lipschitz_const)
+                     lipschitz_const,
+                     tau)
     elif device == 'gpu' and gpu_enabled:
         return TV_PD_CPU(inputData,
                      regularisation_parameter,
@@ -69,7 +70,8 @@ def PD_TV(inputData, regularisation_parameter, iterations,
                      tolerance_param,
                      methodTV,
                      nonneg,
-                     lipschitz_const)
+                     lipschitz_const,
+                     tau)
     else:
         if not gpu_enabled and device == 'gpu':
             raise ValueError ('GPU is not available')
diff --git a/src/Python/src/cpu_regularisers.pyx b/src/Python/src/cpu_regularisers.pyx
index 08e247c..8de6aea 100644
--- a/src/Python/src/cpu_regularisers.pyx
+++ b/src/Python/src/cpu_regularisers.pyx
@@ -20,7 +20,7 @@ cimport numpy as np
 
 cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float *infovector, float *lambdaPar, int lambda_is_arr, int iterationsNumb, float tau, float epsil, int dimX, int dimY, int dimZ);
 cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float *infovector, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int dimX, int dimY, int dimZ);
-cdef extern float PDTV_CPU_main(float *Input, float *U, float *infovector, float lambdaPar, int iterationsNumb, float epsil, float lipschitz_const, int methodTV, int nonneg, int dimX, int dimY, int dimZ);
+cdef extern float PDTV_CPU_main(float *Input, float *U, float *infovector, float lambdaPar, int iterationsNumb, float epsil, float lipschitz_const, int methodTV, int nonneg, float tau, int dimX, int dimY, int dimZ);
 cdef extern float SB_TV_CPU_main(float *Input, float *Output, float *infovector, float mu, int iter, float epsil, int methodTV, int dimX, int dimY, int dimZ);
 cdef extern float LLT_ROF_CPU_main(float *Input, float *Output, float *infovector, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, float epsil, int dimX, int dimY, int dimZ);
 cdef extern float TGV_main(float *Input, float *Output, float *infovector, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, float epsil, int dimX, int dimY, int dimZ);
@@ -159,11 +159,11 @@ def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
 #****************************************************************#
 #****************** Total-variation Primal-dual *****************#
 #****************************************************************#
-def TV_PD_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const):
+def TV_PD_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const, tau):
     if inputData.ndim == 2:
-        return TV_PD_2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const)
+        return TV_PD_2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const, tau)
     elif inputData.ndim == 3:
-        return TV_PD_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const)
+        return TV_PD_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const, tau)
 
 def TV_PD_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                      float regularisation_parameter,
@@ -171,7 +171,8 @@ def TV_PD_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                      float tolerance_param,
                      int methodTV,
                      int nonneg,
-                     float lipschitz_const):
+                     float lipschitz_const,
+                     float tau):
 
     cdef long dims[2]
     dims[0] = inputData.shape[0]
@@ -190,6 +191,7 @@ def TV_PD_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                        lipschitz_const,
                        methodTV,
                        nonneg,
+                       tau,
                        dims[1],dims[0], 1)
     return (outputData,infovec)
 
@@ -198,8 +200,9 @@ def TV_PD_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
                      int iterationsNumb,
                      float tolerance_param,
                      int methodTV,
-                     int nonneg,
-                     float lipschitz_const):
+                     int nonneg,                     
+                     float lipschitz_const,
+                     float tau):
 
     cdef long dims[3]
     dims[0] = inputData.shape[0]
@@ -218,6 +221,7 @@ def TV_PD_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
                        lipschitz_const,
                        methodTV,
                        nonneg,
+                       tau,
                        dims[2], dims[1], dims[0])
     return (outputData,infovec)