dTV method added

9 files changed, 529 insertions, 21 deletions

diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py
index 039daab..c6723fa 100644
--- a/Wrappers/Python/ccpi/filters/regularisers.py
+++ b/Wrappers/Python/ccpi/filters/regularisers.py
@@ -2,8 +2,8 @@
 script which assigns a proper device core function based on a flag ('cpu' or 'gpu')
 """
 
-from ccpi.filters.cpu_regularisers_cython import TV_ROF_CPU, TV_FGP_CPU
-from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU
+from ccpi.filters.cpu_regularisers_cython import TV_ROF_CPU, TV_FGP_CPU dTV_FGP_CPU
+from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU dTV_FGP_GPU
 
 def ROF_TV(inputData, regularisation_parameter, iterations,
                      time_marching_parameter,device='cpu'):
@@ -42,3 +42,28 @@ def FGP_TV(inputData, regularisation_parameter,iterations,
     else:
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
+def FGP_dTV(inputData, refdata, regularisation_parameter, iterations,
+                     tolerance_param, eta_const, methodTV, nonneg, printM, device='cpu'):
+    if device == 'cpu':
+        return dTV_FGP_CPU(inputData,
+                     refdata,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     eta_const,
+                     methodTV,
+                     nonneg,
+                     printM)
+    elif device == 'gpu':
+        return dTV_FGP_GPU(inputData,
+                     refdata,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     eta_const,
+                     methodTV,
+                     nonneg,
+                     printM)
+    else:
+        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+                         .format(device))
diff --git a/Wrappers/Python/test/run_test.py b/Wrappers/Python/conda-recipe/run_test.py
index 04bbd40..04bbd40 100644
--- a/Wrappers/Python/test/run_test.py
+++ b/Wrappers/Python/conda-recipe/run_test.py
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py
index 929f0af..fd3050c 100644
--- a/Wrappers/Python/demos/demo_cpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_regularisers.py
@@ -12,7 +12,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import os
 import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, FGP_dTV
 from qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -39,9 +39,14 @@ perc = 0.05
 u0 = Im + np.random.normal(loc = 0 ,
                                   scale = perc * Im , 
                                   size = np.shape(Im))
+u_ref = Im + np.random.normal(loc = 0 ,
+                                  scale = 0.01 * Im , 
+                                  size = np.shape(Im))
+ 
 # map the u0 u0->u0>0
 # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
 u0 = u0.astype('float32')
+u_ref = u_ref.astype('float32')
 
 
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
@@ -134,9 +139,64 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(fgp_cpu, cmap="gray")
 plt.title('{}'.format('CPU results'))
 
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_____________FGP-dTV (2D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(3)
+plt.suptitle('Performance of FGP-dTV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_dTV, \
+        'input' : u0,\
+        'refdata' : u_ref,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :2000 ,\
+        'tolerance_constant':1e-06,\
+        'eta_const':0.2,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP dTV CPU####################")
+start_time = timeit.default_timer()
+fgp_dtv_cpu = FGP_dTV(pars['input'], 
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')
+             
+rms = rmse(Im, fgp_dtv_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dtv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+
+
 # Uncomment to test 3D regularisation performance 
 #%%
-"""
+
 N = 512
 slices = 20
 
@@ -148,10 +208,12 @@ Im = Im/255
 perc = 0.05
 
 noisyVol = np.zeros((slices,N,N),dtype='float32')
+noisyRef = np.zeros((slices,N,N),dtype='float32')
 idealVol = np.zeros((slices,N,N),dtype='float32')
 
 for i in range (slices):
     noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im))
+    noisyRef[i,:,:] = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im))
     idealVol[i,:,:] = Im
 
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
@@ -159,7 +221,7 @@ print ("_______________ROF-TV (3D)_________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(3)
+fig = plt.figure(4)
 plt.suptitle('Performance of ROF-TV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy 15th slice of a volume')
@@ -199,7 +261,7 @@ print ("_______________FGP-TV (3D)__________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(4)
+fig = plt.figure(5)
 plt.suptitle('Performance of FGP-TV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -242,5 +304,58 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
          verticalalignment='top', bbox=props)
 imgplot = plt.imshow(fgp_cpu3D[10,:,:], cmap="gray")
 plt.title('{}'.format('Recovered volume on the CPU using FGP-TV'))
-"""
-#%%
\ No newline at end of file
+
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________FGP-dTV (3D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(6)
+plt.suptitle('Performance of FGP-dTV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_dTV, \
+        'input' : noisyVol,\ 
+        'refdata' : noisyRef,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :300 ,\
+        'tolerance_constant':0.00001,\
+        'eta_const':0.2,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP dTV CPU####################")
+start_time = timeit.default_timer()
+fgp_dTV_cpu3D = FGP_dTV(pars['input'],
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'],
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')
+             
+             
+rms = rmse(idealVol, fgp_dTV_cpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dTV_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using FGP-dTV'))
+#%%
diff --git a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
index cfe2e7d..aa1f865 100644
--- a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
@@ -12,7 +12,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import os
 import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, FGP_dTV
 from qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -39,10 +39,14 @@ perc = 0.05
 u0 = Im + np.random.normal(loc = 0 ,
                                   scale = perc * Im , 
                                   size = np.shape(Im))
+u_ref = Im + np.random.normal(loc = 0 ,
+                                  scale = 0.01 * Im , 
+                                  size = np.shape(Im))
+
 # map the u0 u0->u0>0
 # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
 u0 = u0.astype('float32')
-
+u_ref = u_ref.astype('float32')
 
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("____________ROF-TV bench___________________")
@@ -213,3 +217,96 @@ else:
     print ("Arrays match")
 
 
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________FGP-dTV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(3)
+plt.suptitle('Comparison of FGP-dTV regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_dTV, \
+        'input' : u0,\
+        'refdata' : u_ref,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :2000 ,\
+        'tolerance_constant':1e-06,\
+        'eta_const':0.2,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP dTV CPU####################")
+start_time = timeit.default_timer()
+fgp_dtv_cpu = FGP_dTV(pars['input'], 
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')
+             
+             
+rms = rmse(Im, fgp_dtv_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dtv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+print ("##############FGP dTV GPU##################")
+start_time = timeit.default_timer()
+fgp_dtv_gpu = FGP_dTV(pars['input'], 
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+rms = rmse(Im, fgp_dtv_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = FGP_dTV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dtv_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(rof_cpu))
+diff_im = abs(fgp_dtv_cpu - fgp_dtv_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py
index c496e1c..4759cc3 100644
--- a/Wrappers/Python/demos/demo_gpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_gpu_regularisers.py
@@ -12,7 +12,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import os
 import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, FGP_dTV
 from qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -39,10 +39,13 @@ perc = 0.05
 u0 = Im + np.random.normal(loc = 0 ,
                                   scale = perc * Im , 
                                   size = np.shape(Im))
+u_ref = Im + np.random.normal(loc = 0 ,
+                                  scale = 0.01 * Im , 
+                                  size = np.shape(Im))
 # map the u0 u0->u0>0
 # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
 u0 = u0.astype('float32')
-
+u_ref = u_ref.astype('float32')
 
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("____________ROF-TV bench___________________")
@@ -134,10 +137,62 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(fgp_gpu, cmap="gray")
 plt.title('{}'.format('GPU results'))
 
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________FGP-dTV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(3)
+plt.suptitle('Performance of the FGP-dTV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_dTV, \
+        'input' : u0,\
+        'refdata' : u_ref,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :2000 ,\
+        'tolerance_constant':1e-06,\
+        'eta_const':0.2,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+
+print ("##############FGP dTV GPU##################")
+start_time = timeit.default_timer()
+fgp_dtv_gpu = FGP_dTV(pars['input'], 
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+                                   
+rms = rmse(Im, fgp_dtv_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = FGP_dTV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dtv_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
 
 # Uncomment to test 3D regularisation performance 
 #%%
-"""
+
 N = 512
 slices = 20
 
@@ -149,10 +204,12 @@ Im = Im/255
 perc = 0.05
 
 noisyVol = np.zeros((slices,N,N),dtype='float32')
+noisyRef = np.zeros((slices,N,N),dtype='float32')
 idealVol = np.zeros((slices,N,N),dtype='float32')
 
 for i in range (slices):
     noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im))
+    noisyRef[i,:,:] = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im))
     idealVol[i,:,:] = Im
 
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
@@ -160,7 +217,7 @@ print ("_______________ROF-TV (3D)_________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(3)
+fig = plt.figure(4)
 plt.suptitle('Performance of ROF-TV regulariser using the GPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy 15th slice of a volume')
@@ -200,7 +257,7 @@ print ("_______________FGP-TV (3D)__________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(4)
+fig = plt.figure(5)
 plt.suptitle('Performance of FGP-TV regulariser using the GPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -242,6 +299,58 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
          verticalalignment='top', bbox=props)
 imgplot = plt.imshow(fgp_gpu3D[10,:,:], cmap="gray")
 plt.title('{}'.format('Recovered volume on the GPU using FGP-TV'))
-#%%
-"""
 
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________FGP-dTV (3D)________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(6)
+plt.suptitle('Performance of FGP-dTV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_dTV, \
+        'input' : noisyVol,\ 
+        'refdata' : noisyRef,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :300 ,\
+        'tolerance_constant':0.00001,\
+        'eta_const':0.2,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+
+print ("#############FGP TV GPU####################")
+start_time = timeit.default_timer()
+fgp_dTV_gpu3D = FGP_dTV(pars['input'],
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'],
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+
+rms = rmse(idealVol, fgp_dTV_gpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dTV_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using FGP-dTV'))
+
+#%%
diff --git a/Wrappers/Python/setup-regularisers.py.in b/Wrappers/Python/setup-regularisers.py.in
index a1c1ab6..c7ebb5c 100644
--- a/Wrappers/Python/setup-regularisers.py.in
+++ b/Wrappers/Python/setup-regularisers.py.in
@@ -36,6 +36,7 @@ extra_include_dirs += [os.path.join(".." , ".." , "Core"),
                        os.path.join(".." , ".." , "Core",  "regularisers_CPU"),
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TV_FGP" ) , 
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TV_ROF" ) , 
+                       os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "dTV_FGP" ) , 
 						   "."]
 
 if platform.system() == 'Windows':				   
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 0f08f7f..8f9185a 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -20,6 +20,7 @@ cimport numpy as np
 
 cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
 cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
+cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
 
 
 #****************************************************************#
@@ -89,7 +90,7 @@ def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
     cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
             np.zeros([dims[0],dims[1]], dtype='float32')
                    
-    #/* Run ROF iterations for 2D data */
+    #/* Run FGP-TV iterations for 2D data */
     TV_FGP_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, 
                        iterationsNumb, 
                        tolerance_param,
@@ -115,7 +116,7 @@ def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
     cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
             np.zeros([dims[0], dims[1], dims[2]], dtype='float32')
            
-    #/* Run ROF iterations for 3D data */
+    #/* Run FGP-TV iterations for 3D data */
     TV_FGP_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter,
                        iterationsNumb, 
                        tolerance_param,
@@ -124,3 +125,69 @@ def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
                        printM,
                        dims[2], dims[1], dims[0])
     return outputData 
+#****************************************************************#
+#**************Directional Total-variation FGP ******************#
+#****************************************************************#
+#******** Directional TV Fast-Gradient-Projection (FGP)*********#
+def dTV_FGP_CPU(inputData, refdata, regularisation_parameter, iterationsNumb, tolerance_param, eta_const, methodTV, nonneg, printM):
+    if inputData.ndim == 2:
+        return dTV_FGP_2D(inputData, refdata, regularisation_parameter, iterationsNumb, tolerance_param, eta_const, methodTV, nonneg, printM)
+    elif inputData.ndim == 3:
+        return dTV_FGP_3D(inputData, refdata, regularisation_parameter, iterationsNumb, tolerance_param, eta_const, methodTV, nonneg, printM)
+
+def dTV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+               np.ndarray[np.float32_t, ndim=2, mode="c"] refdata,
+                     float regularisation_parameter,
+                     int iterationsNumb, 
+                     float tolerance_param,
+                     float eta_const,
+                     int methodTV,
+                     int nonneg,
+                     int printM):
+                         
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+                   
+    #/* Run FGP-dTV iterations for 2D data */
+    dTV_FGP_CPU_main(&inputData[0,0], &refdata[0,0], &outputData[0,0], regularisation_parameter, 
+                       iterationsNumb, 
+                       tolerance_param,
+                       methodTV,
+                       eta_const,
+                       nonneg,
+                       printM,
+                       dims[0], dims[1], 1)
+    
+    return outputData        
+            
+def dTV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
+               np.ndarray[np.float32_t, ndim=3, mode="c"] refdata,
+                     float regularisation_parameter,
+                     int iterationsNumb, 
+                     float tolerance_param,
+                     float eta_const,
+                     int methodTV,
+                     int nonneg,
+                     int printM):
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+            np.zeros([dims[0], dims[1], dims[2]], dtype='float32')
+           
+    #/* Run FGP-dTV iterations for 3D data */
+    dTV_FGP_CPU_main(&inputData[0,0,0], &refdata[0,0,0], &outputData[0,0,0], regularisation_parameter,
+                       iterationsNumb, 
+                       tolerance_param,
+                       eta_const,
+                       methodTV,
+                       nonneg,
+                       printM,
+                       dims[2], dims[1], dims[0])
+    return outputData
diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx
index ea746d3..4a14f69 100644
--- a/Wrappers/Python/src/gpu_regularisers.pyx
+++ b/Wrappers/Python/src/gpu_regularisers.pyx
@@ -20,6 +20,7 @@ cimport numpy as np
 
 cdef extern void TV_ROF_GPU_main(float* Input, float* Output, float lambdaPar, int iter, float tau, int N, int M, int Z);
 cdef extern void TV_FGP_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int N, int M, int Z);
+cdef extern void dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int N, int M, int Z);
 
 # Total-variation Rudin-Osher-Fatemi (ROF)
 def TV_ROF_GPU(inputData,
@@ -61,7 +62,36 @@ def TV_FGP_GPU(inputData,
                      methodTV,
                      nonneg,
                      printM)
-                     
+# Directional Total-variation Fast-Gradient-Projection (FGP)
+def dTV_FGP_GPU(inputData,
+                     refdata,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     eta_const,
+                     methodTV,
+                     nonneg,
+                     printM):
+    if inputData.ndim == 2:
+        return FGPdTV2D(inputData,
+                     refdata,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     eta_const,
+                     methodTV,
+                     nonneg,
+                     printM)
+    elif inputData.ndim == 3:
+        return FGPdTV3D(inputData,
+                     refdata,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     eta_const,
+                     methodTV,
+                     nonneg,
+                     printM)
 #****************************************************************#
 #********************** Total-variation ROF *********************#
 #****************************************************************#
@@ -167,4 +197,68 @@ def FGPTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
                        printM,
                        dims[2], dims[1], dims[0]);   
      
-    return outputData    
+    return outputData 
+    
+#****************************************************************#
+#**************Directional Total-variation FGP ******************#
+#****************************************************************#
+#******** Directional TV Fast-Gradient-Projection (FGP)*********#
+def FGPdTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+             np.ndarray[np.float32_t, ndim=3, mode="c"] refdata,
+                     float regularisation_parameter,
+                     int iterations, 
+                     float tolerance_param,
+                     float eta_const,
+                     int methodTV,
+                     int nonneg,
+                     int printM):
+    
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1]], dtype='float32')
+          
+    # Running CUDA code here    
+    dTV_FGP_GPU_main(&inputData[0,0], &refdata[0,0], &outputData[0,0],                        
+                       regularisation_parameter, 
+                       iterations, 
+                       tolerance_param,
+                       eta_const,
+                       methodTV,
+                       nonneg,
+                       printM,
+                       dims[0], dims[1], 1);   
+     
+    return outputData
+    
+def FGPdTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+             np.ndarray[np.float32_t, ndim=3, mode="c"] refdata, 
+                     float regularisation_parameter,
+                     int iterations, 
+                     float tolerance_param,
+                     float eta_const,
+                     int methodTV,
+                     int nonneg,
+                     int printM):
+    
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+          
+    # Running CUDA code here    
+    dTV_FGP_GPU_main(&inputData[0,0,0], &refdata[0,0,0], &outputData[0,0,0], 
+                       regularisation_parameter , 
+                       iterations, 
+                       tolerance_param,
+                       eta_const,
+                       methodTV,
+                       nonneg,
+                       printM,
+                       dims[2], dims[1], dims[0]);
+    return outputData 
diff --git a/Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc b/Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc
deleted file mode 100644
index 2196a53..0000000
--- a/Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc
+++ /dev/null