diff options
Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Python/ccpi/plugins/regularisers.py | 12 | ||||
| -rw-r--r-- | Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py | 35 |
2 files changed, 31 insertions, 16 deletions
diff --git a/Wrappers/Python/ccpi/plugins/regularisers.py b/Wrappers/Python/ccpi/plugins/regularisers.py index 6d865cc..9f4d3fc 100644 --- a/Wrappers/Python/ccpi/plugins/regularisers.py +++ b/Wrappers/Python/ccpi/plugins/regularisers.py @@ -32,10 +32,10 @@ class _ROF_TV_(Operator): self.iterationsTV = iterationsTV self.time_marchstep = time_marchstep self.device = device # string for 'cpu' or 'gpu' - def __call__(self,x,x1,typeEnergy): + def __call__(self,x): # evaluate objective function of TV gradient # typeEnergy is either 1 (LS + TV for denoising) or 2 (just TV fidelity) - EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x1.as_array(), dtype=np.float32), self.lambdaReg, typeEnergy) + EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2) return EnergyValTV def prox(self,x,Lipshitz): pars = {'algorithm' : ROF_TV, \ @@ -60,10 +60,10 @@ class _FGP_TV_(Operator): self.nonnegativity = nonnegativity self.printing = printing self.device = device # string for 'cpu' or 'gpu' - def __call__(self,x,x1,typeEnergy): + def __call__(self,x): # evaluate objective function of TV gradient # typeEnergy is either 1 (LS + TV for denoising) or 2 (just TV fidelity) - EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x1.as_array(), dtype=np.float32), self.lambdaReg, typeEnergy) + EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2) return EnergyValTV def prox(self,x,Lipshitz): pars = {'algorithm' : FGP_TV, \ @@ -94,10 +94,10 @@ class _SB_TV_(Operator): self.methodTV = methodTV self.printing = printing self.device = device # string for 'cpu' or 'gpu' - def __call__(self,x,typeEnergy): + def __call__(self,x): # evaluate objective function of TV gradient # typeEnergy is either 1 (LS + TV for denoising) or 2 (just TV fidelity) - EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, typeEnergy) + EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2) return EnergyValTV def prox(self,x,Lipshitz): pars = {'algorithm' : SB_TV, \ diff --git a/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py b/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py index dd9044e..07f4090 100644 --- a/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py +++ b/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py @@ -5,7 +5,7 @@ from ccpi.optimisation.funcs import Norm2sq, ZeroFun, Norm1, TV2D, Identity from ccpi.optimisation.ops import LinearOperatorMatrix -from ccpi.plugins.regularisers import _ROF_TV_, _FGP_TV_ +from ccpi.plugins.regularisers import _ROF_TV_, _FGP_TV_, _SB_TV_ import numpy as np import matplotlib.pyplot as plt @@ -72,12 +72,12 @@ if use_cvxpy: # print(xtv_denoise.value) # print(objectivetv_denoise.value) +plt.figure() plt.imshow(xtv_denoise.value) -plt.title('CVX TV') +plt.title('CVX TV with objective equal to {:.2f}'.format(objectivetv_denoise.value)) plt.show() print(objectivetv_denoise.value) - #%% THen FBPD # Data fidelity term @@ -97,7 +97,7 @@ x_fbpdtv_denoise, itfbpdtv_denoise, timingfbpdtv_denoise, criterfbpdtv_denoise = print("CVXPY least squares plus TV solution and objective value:") plt.figure() plt.imshow(x_fbpdtv_denoise.as_array()) -plt.title('FBPD TV') +plt.title('FBPD TV with objective equal to {:.2f}'.format(criterfbpdtv_denoise[-1])) plt.show() print(criterfbpdtv_denoise[-1]) @@ -108,30 +108,45 @@ plt.loglog(criterfbpdtv_denoise, label='FBPD TV') plt.show() #%% FISTA with ROF-TV regularisation -g_rof = _ROF_TV_(lambdaReg = lam_tv,iterationsTV=5000,tolerance=0,time_marchstep=0.001,device='cpu') +g_rof = _ROF_TV_(lambdaReg = lam_tv,iterationsTV=5000,tolerance=0,time_marchstep=0.0009,device='cpu') xtv_rof = g_rof.prox(y,1.0) print("CCPi-RGL TV ROF:") plt.figure() plt.imshow(xtv_rof.as_array()) -plt.title('ROF TV prox') +valObjRof = g_rof(xtv_rof) +plt.title('ROF TV prox with objective equal to {:.2f}'.format(valObjRof[0])) plt.show() -print(g_rof(xtv_rof,y,typeEnergy=1)) +print(valObjRof[0]) #%% FISTA with FGP-TV regularisation -g_fgp = _FGP_TV_(lambdaReg = lam_tv,iterationsTV=5000,tolerance=1e-7,methodTV=0,nonnegativity=0,printing=0,device='cpu') +g_fgp = _FGP_TV_(lambdaReg = lam_tv,iterationsTV=5000,tolerance=0,methodTV=0,nonnegativity=0,printing=0,device='cpu') xtv_fgp = g_fgp.prox(y,1.0) print("CCPi-RGL TV FGP:") plt.figure() plt.imshow(xtv_fgp.as_array()) -plt.title('FGP TV prox') +valObjFGP = g_fgp(xtv_fgp) +plt.title('FGP TV prox with objective equal to {:.2f}'.format(valObjFGP[0])) plt.show() -print(g_fgp(xtv_fgp,y,typeEnergy=1)) +print(valObjFGP[0]) +#%% Split-Bregman-TV regularisation +g_sb = _SB_TV_(lambdaReg = lam_tv,iterationsTV=150,tolerance=0,methodTV=0,printing=0,device='cpu') +xtv_sb = g_sb.prox(y,1.0) + +print("CCPi-RGL TV SB:") +plt.figure() +plt.imshow(xtv_sb.as_array()) +valObjSB = g_sb(xtv_sb) +plt.title('SB TV prox with objective equal to {:.2f}'.format(valObjSB[0])) +plt.show() +print(valObjSB[0]) #%% + + # Compare all reconstruction clims = (-0.2,1.2) dlims = (-0.2,0.2) |