From 0dd1cadcfead9a2a5f225e1500c97cc00a8068d6 Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Fri, 11 May 2018 12:44:18 +0100 Subject: some fixes regarding denoising --- Wrappers/Python/ccpi/plugins/regularisers.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) (limited to 'Wrappers/Python/ccpi') diff --git a/Wrappers/Python/ccpi/plugins/regularisers.py b/Wrappers/Python/ccpi/plugins/regularisers.py index e9c88a4..6d865cc 100644 --- a/Wrappers/Python/ccpi/plugins/regularisers.py +++ b/Wrappers/Python/ccpi/plugins/regularisers.py @@ -25,7 +25,6 @@ from ccpi.optimisation.ops import Operator import numpy as np - class _ROF_TV_(Operator): def __init__(self,lambdaReg,iterationsTV,tolerance,time_marchstep,device): # set parameters @@ -33,9 +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): + def __call__(self,x,x1,typeEnergy): # evaluate objective function of TV gradient - EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2) + # 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) return EnergyValTV def prox(self,x,Lipshitz): pars = {'algorithm' : ROF_TV, \ @@ -60,9 +60,10 @@ class _FGP_TV_(Operator): self.nonnegativity = nonnegativity self.printing = printing self.device = device # string for 'cpu' or 'gpu' - def __call__(self,x): + def __call__(self,x,x1,typeEnergy): # evaluate objective function of TV gradient - EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2) + # 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) return EnergyValTV def prox(self,x,Lipshitz): pars = {'algorithm' : FGP_TV, \ @@ -93,9 +94,10 @@ class _SB_TV_(Operator): self.methodTV = methodTV self.printing = printing self.device = device # string for 'cpu' or 'gpu' - def __call__(self,x): + def __call__(self,x,typeEnergy): # evaluate objective function of TV gradient - EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2) + # 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) return EnergyValTV def prox(self,x,Lipshitz): pars = {'algorithm' : SB_TV, \ -- cgit v1.2.3 From cfd675c4dcc9ca090e6401f70c68bdc34da004db Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Fri, 11 May 2018 15:42:35 +0100 Subject: further corrections, SB-TV added --- Wrappers/Python/ccpi/plugins/regularisers.py | 12 ++++---- .../Python/wip/demo_compare_RGLTK_TV_denoising.py | 35 +++++++++++++++------- 2 files changed, 31 insertions(+), 16 deletions(-) (limited to 'Wrappers/Python/ccpi') 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) -- cgit v1.2.3 From d1875172687fc854df35fa9bfc6ac07a148d7f18 Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Sat, 12 May 2018 19:03:26 +0100 Subject: fixed objective2 --- Wrappers/Python/ccpi/plugins/regularisers.py | 9 +++------ Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py | 18 +++++++----------- 2 files changed, 10 insertions(+), 17 deletions(-) (limited to 'Wrappers/Python/ccpi') diff --git a/Wrappers/Python/ccpi/plugins/regularisers.py b/Wrappers/Python/ccpi/plugins/regularisers.py index 9f4d3fc..46464a9 100644 --- a/Wrappers/Python/ccpi/plugins/regularisers.py +++ b/Wrappers/Python/ccpi/plugins/regularisers.py @@ -34,9 +34,8 @@ class _ROF_TV_(Operator): self.device = device # string for 'cpu' or 'gpu' 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, 2) - return EnergyValTV + return 0.5*EnergyValTV[0] def prox(self,x,Lipshitz): pars = {'algorithm' : ROF_TV, \ 'input' : np.asarray(x.as_array(), dtype=np.float32),\ @@ -62,9 +61,8 @@ class _FGP_TV_(Operator): self.device = device # string for 'cpu' or 'gpu' 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, 2) - return EnergyValTV + return 0.5*EnergyValTV[0] def prox(self,x,Lipshitz): pars = {'algorithm' : FGP_TV, \ 'input' : np.asarray(x.as_array(), dtype=np.float32),\ @@ -96,9 +94,8 @@ class _SB_TV_(Operator): self.device = device # string for 'cpu' or 'gpu' 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, 2) - return EnergyValTV + return 0.5*EnergyValTV[0] def prox(self,x,Lipshitz): pars = {'algorithm' : SB_TV, \ 'input' : np.asarray(x.as_array(), dtype=np.float32),\ diff --git a/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py b/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py index 0d57e5e..2bf1286 100644 --- a/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py +++ b/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py @@ -75,11 +75,13 @@ plt.title('CVX TV with objective equal to {:.2f}'.format(objectivetv_denoise.va plt.show() print(objectivetv_denoise.value) -#%% THen FBPD - +#%% # Data fidelity term f_denoise = Norm2sq(I,y,c=0.5) +#%% + +#%% THen FBPD # Initial guess x_init_denoise = ImageData(np.zeros((N,N))) @@ -112,9 +114,7 @@ xtv_rof = g_rof.prox(y,1.0) print("CCPi-RGL TV ROF:") plt.figure() plt.imshow(xtv_rof.as_array()) -valObjRof = g_rof(xtv_rof) -data_energy = 0.5*np.sum(np.power((xtv_rof.as_array() - y.array),2)) -EnergytotalROF = data_energy + 0.5*valObjRof[0] +EnergytotalROF = f_denoise(xtv_rof) + g_rof(xtv_rof) plt.title('ROF TV prox with objective equal to {:.2f}'.format(EnergytotalROF)) plt.show() print(EnergytotalROF) @@ -127,9 +127,7 @@ xtv_fgp = g_fgp.prox(y,1.0) print("CCPi-RGL TV FGP:") plt.figure() plt.imshow(xtv_fgp.as_array()) -valObjFGP = g_fgp(xtv_fgp) -data_energy = 0.5*np.sum(np.power((xtv_fgp.as_array() - y.array),2)) -EnergytotalFGP = data_energy + 0.5*valObjFGP[0] +EnergytotalFGP = f_denoise(xtv_fgp) + g_fgp(xtv_fgp) plt.title('FGP TV prox with objective equal to {:.2f}'.format(EnergytotalFGP)) plt.show() print(EnergytotalFGP) @@ -141,9 +139,7 @@ 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) -data_energy = 0.5*np.sum(np.power((xtv_sb.as_array() - y.array),2)) -EnergytotalSB = data_energy + 0.5*valObjSB[0] +EnergytotalSB = f_denoise(xtv_sb) + g_fgp(xtv_sb) plt.title('SB TV prox with objective equal to {:.2f}'.format(EnergytotalSB)) plt.show() print(EnergytotalSB) -- cgit v1.2.3