all work completed on gpu version of pdtv

7 files changed, 244 insertions, 9 deletions

diff --git a/demos/Matlab_demos/demoMatlab_3Ddenoise.m b/demos/Matlab_demos/demoMatlab_3Ddenoise.m
index f018327..b7f92cb 100644
--- a/demos/Matlab_demos/demoMatlab_3Ddenoise.m
+++ b/demos/Matlab_demos/demoMatlab_3Ddenoise.m
@@ -62,6 +62,25 @@ fprintf('Denoise a volume using the FGP-TV model (GPU) \n');
 % fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgpG);
 % figure; imshow(u_fgpG(:,:,7), [0 1]); title('FGP-TV denoised volume (GPU)');
 %%
+fprintf('Denoise a volume using the PD-TV model (CPU) \n');
+lambda_reg = 0.03; % regularsation parameter for all methods
+iter_pd = 300; % number of FGP iterations
+epsil_tol =  0.0; % tolerance
+tic; [u_pd,infovec] = PD_TV(single(vol3D), lambda_reg, iter_pd, epsil_tol); toc; 
+energyfunc_val_fgp = TV_energy(single(u_pd),single(vol3D),lambda_reg, 1); % get energy function value
+rmse_pd = (RMSE(Ideal3D(:),u_pd(:)));
+fprintf('%s %f \n', 'RMSE error for PD-TV is:', rmse_pd);
+figure; imshow(u_pd(:,:,7), [0 1]); title('PD-TV denoised volume (CPU)');
+%%
+% fprintf('Denoise a volume using the PD-TV model (GPU) \n');
+% lambda_reg = 0.03; % regularsation parameter for all methods
+% iter_pd = 300; % number of FGP iterations
+% epsil_tol =  0.0; % tolerance
+% tic; u_pdG = PD_TV_GPU(single(vol3D), lambda_reg, iter_pd, epsil_tol); toc; 
+% rmse_pdG = (RMSE(Ideal3D(:),u_pdG(:)));
+% fprintf('%s %f \n', 'RMSE error for PD-TV is:', rmse_pdG);
+% figure; imshow(u_pdG(:,:,7), [0 1]); title('PD-TV denoised volume (GPU)');
+%%
 fprintf('Denoise a volume using the SB-TV model (CPU) \n');
 iter_sb = 150; % number of SB iterations
 epsil_tol =  0.0; % tolerance
diff --git a/demos/Matlab_demos/demoMatlab_denoise.m b/demos/Matlab_demos/demoMatlab_denoise.m
index b50eaf5..3d93cb6 100644
--- a/demos/Matlab_demos/demoMatlab_denoise.m
+++ b/demos/Matlab_demos/demoMatlab_denoise.m
@@ -46,6 +46,22 @@ figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)');
 % tic; u_fgpG = FGP_TV_GPU(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; 
 % figure; imshow(u_fgpG, [0 1]); title('FGP-TV denoised image (GPU)');
 %%
+fprintf('Denoise using the PD-TV model (CPU) \n');
+lambda_reg = 0.03;
+iter_pd = 500; % number of FGP iterations
+epsil_tol =  0.0; % tolerance
+tic; [u_pd,infovec] = PD_TV(single(u0), lambda_reg, iter_pd, epsil_tol); toc; 
+energyfunc_val_pd = TV_energy(single(u_pd),single(u0),lambda_reg, 1); % get energy function value
+rmsePD = (RMSE(u_pd(:),Im(:)));
+fprintf('%s %f \n', 'RMSE error for PD-TV is:', rmsePD);
+[ssimval] = ssim(u_pd*255,single(Im)*255);
+fprintf('%s %f \n', 'MSSIM error for PD-TV is:', ssimval);
+figure; imshow(u_pd, [0 1]); title('PD-TV denoised image (CPU)');
+%%
+% fprintf('Denoise using the PD-TV model (GPU) \n');
+% tic; u_pdG = PD_TV_GPU(single(u0), lambda_reg, iter_pd, epsil_tol); toc; 
+% figure; imshow(u_pdG, [0 1]); title('PD-TV denoised image (GPU)');
+%%
 fprintf('Denoise using the SB-TV model (CPU) \n');
 lambda_reg = 0.03;
 iter_sb = 200; % number of SB iterations
diff --git a/demos/demo_cpu_regularisers.py b/demos/demo_cpu_regularisers.py
index 09781d5..7d66b7f 100644
--- a/demos/demo_cpu_regularisers.py
+++ b/demos/demo_cpu_regularisers.py
@@ -130,7 +130,7 @@ pars = {'algorithm' : FGP_TV, \
         'input' : u0,\
         'regularisation_parameter':0.02, \
         'number_of_iterations' :1500 ,\
-        'tolerance_constant':1e-06,\
+        'tolerance_constant':1e-08,\
         'methodTV': 0 ,\
         'nonneg': 0}
         
@@ -176,7 +176,7 @@ pars = {'algorithm' : PD_TV, \
         'input' : u0,\
         'regularisation_parameter':0.02, \
         'number_of_iterations' :1500 ,\
-        'tolerance_constant':1e-06,\
+        'tolerance_constant':1e-08,\
         'methodTV': 0 ,\
         'nonneg': 1,
         'lipschitz_const' : 8,
diff --git a/demos/demo_cpu_regularisers3D.py b/demos/demo_cpu_regularisers3D.py
index 349300f..cfdd2d4 100644
--- a/demos/demo_cpu_regularisers3D.py
+++ b/demos/demo_cpu_regularisers3D.py
@@ -185,10 +185,11 @@ pars = {'algorithm' : PD_TV, \
         'input' : noisyVol,\
         'regularisation_parameter':0.02, \
         'number_of_iterations' :1000 ,\
-        'tolerance_constant': 1e-06,\
+        'tolerance_constant':1e-06,\
         'methodTV': 0 ,\
-        'lipschitz_const' : 12,\
-        'nonneg': 0}
+        'nonneg': 0,
+        'lipschitz_const' : 8,
+        'tau' : 0.0025}
 
 print ("#############FGP TV GPU####################")
 start_time = timeit.default_timer()
@@ -198,7 +199,8 @@ start_time = timeit.default_timer()
               pars['tolerance_constant'], 
               pars['methodTV'],
               pars['nonneg'],
-              pars['lipschitz_const'], 'cpu')
+              pars['lipschitz_const'],
+              pars['tau'],'cpu')
              
 Qtools = QualityTools(idealVol, pd_cpu3D)
 pars['rmse'] = Qtools.rmse()
diff --git a/demos/demo_cpu_vs_gpu_regularisers.py b/demos/demo_cpu_vs_gpu_regularisers.py
index 21e3899..015dfc6 100644
--- a/demos/demo_cpu_vs_gpu_regularisers.py
+++ b/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, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, PD_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
 from ccpi.filters.regularisers import PatchSelect
 from ccpi.supp.qualitymetrics import QualityTools
 ###############################################################################
@@ -220,6 +220,108 @@ if (diff_im.sum() > 1):
 else:
     print ("Arrays match")
 
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________PD-TV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of PD-TV 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' : PD_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.02, \
+        'number_of_iterations' :1500 ,\
+        'tolerance_constant':0.0,\
+        'methodTV': 0 ,\
+        'nonneg': 0,
+        'lipschitz_const' : 8,
+        'tau' : 0.0025}
+        
+print ("#############PD TV CPU####################")
+start_time = timeit.default_timer()
+(pd_cpu,info_vec_cpu) = PD_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['lipschitz_const'],
+              pars['tau'],'cpu')
+
+Qtools = QualityTools(Im, pd_cpu)
+pars['rmse'] = Qtools.rmse()
+
+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(pd_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+# set parameters
+pars = {'algorithm' : PD_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.02, \
+        'number_of_iterations' :1500 ,\
+        'tolerance_constant':0.0,\
+        'methodTV': 0 ,\
+        'nonneg': 0,
+        'lipschitz_const' : 8,
+        'tau' : 0.0025}
+        
+print ("#############PD TV CPU####################")
+start_time = timeit.default_timer()
+(pd_gpu,info_vec_gpu) = PD_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['lipschitz_const'],
+              pars['tau'],'gpu')
+
+Qtools = QualityTools(Im, pd_gpu)
+pars['rmse'] = Qtools.rmse()
+
+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(pd_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(pd_cpu))
+diff_im = abs(pd_cpu - pd_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")
 #%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("____________SB-TV bench___________________")
diff --git a/demos/demo_gpu_regularisers.py b/demos/demo_gpu_regularisers.py
index 3efcfce..5131847 100644
--- a/demos/demo_gpu_regularisers.py
+++ b/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, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, PD_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
 from ccpi.filters.regularisers import PatchSelect, NLTV
 from ccpi.supp.qualitymetrics import QualityTools
 ###############################################################################
@@ -158,6 +158,55 @@ imgplot = plt.imshow(fgp_gpu, cmap="gray")
 plt.title('{}'.format('GPU results'))
 #%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________PD-TV (2D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of PD-TV 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' : PD_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.02, \
+        'number_of_iterations' :1500 ,\
+        'tolerance_constant':1e-06,\
+        'methodTV': 0 ,\
+        'nonneg': 1,
+        'lipschitz_const' : 8,
+        'tau' : 0.0025}
+        
+print ("#############PD TV CPU####################")
+start_time = timeit.default_timer()
+(pd_gpu,info_vec_gpu) = PD_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['lipschitz_const'],
+              pars['tau'],'gpu')
+
+Qtools = QualityTools(Im, pd_gpu)
+pars['rmse'] = Qtools.rmse()
+
+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(pd_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("____________SB-TV regulariser______________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
diff --git a/demos/demo_gpu_regularisers3D.py b/demos/demo_gpu_regularisers3D.py
index ccf9694..2c25d01 100644
--- a/demos/demo_gpu_regularisers3D.py
+++ b/demos/demo_gpu_regularisers3D.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, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, PD_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
 from ccpi.supp.qualitymetrics import QualityTools
 ###############################################################################
 def printParametersToString(pars):
@@ -172,7 +172,54 @@ 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 ("_______________PD-TV (3D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of PD-TV 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' : PD_TV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.02, \
+        'number_of_iterations' :1000 ,\
+        'tolerance_constant':1e-06,\
+        'methodTV': 0 ,\
+        'nonneg': 0,
+        'lipschitz_const' : 8,
+        'tau' : 0.0025}
+
+print ("#############PD TV GPU####################")
+start_time = timeit.default_timer()
+(pd_gpu3D, info_vec_gpu)  = PD_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['lipschitz_const'],
+              pars['tau'],'gpu')
+
+Qtools = QualityTools(idealVol, pd_gpu3D)
+pars['rmse'] = Qtools.rmse()
+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(pd_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using PD-TV'))
 #%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________SB-TV (3D)__________________")