diff options
| -rw-r--r-- | Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py | 29 |
1 files changed, 16 insertions, 13 deletions
diff --git a/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py b/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py index 07f4090..0d57e5e 100644 --- a/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py +++ b/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py @@ -1,16 +1,13 @@ from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, DataContainer from ccpi.optimisation.algs import FISTA, FBPD, CGLS -from ccpi.optimisation.funcs import Norm2sq, ZeroFun, Norm1, TV2D, Identity - -from ccpi.optimisation.ops import LinearOperatorMatrix +from ccpi.optimisation.funcs import Norm2sq, ZeroFun, Norm1, TV2D +from ccpi.optimisation.ops import LinearOperatorMatrix, Identity from ccpi.plugins.regularisers import _ROF_TV_, _FGP_TV_, _SB_TV_ import numpy as np import matplotlib.pyplot as plt - - #%% # Requires CVXPY, see http://www.cvxpy.org/ # CVXPY can be installed in anaconda using @@ -108,7 +105,7 @@ 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=0.0009,device='cpu') +g_rof = _ROF_TV_(lambdaReg = lam_tv,iterationsTV=2000,tolerance=0,time_marchstep=0.0009,device='cpu') xtv_rof = g_rof.prox(y,1.0) @@ -116,9 +113,11 @@ print("CCPi-RGL TV ROF:") plt.figure() plt.imshow(xtv_rof.as_array()) valObjRof = g_rof(xtv_rof) -plt.title('ROF TV prox with objective equal to {:.2f}'.format(valObjRof[0])) +data_energy = 0.5*np.sum(np.power((xtv_rof.as_array() - y.array),2)) +EnergytotalROF = data_energy + 0.5*valObjRof[0] +plt.title('ROF TV prox with objective equal to {:.2f}'.format(EnergytotalROF)) plt.show() -print(valObjRof[0]) +print(EnergytotalROF) #%% FISTA with FGP-TV regularisation g_fgp = _FGP_TV_(lambdaReg = lam_tv,iterationsTV=5000,tolerance=0,methodTV=0,nonnegativity=0,printing=0,device='cpu') @@ -129,11 +128,13 @@ print("CCPi-RGL TV FGP:") plt.figure() plt.imshow(xtv_fgp.as_array()) valObjFGP = g_fgp(xtv_fgp) -plt.title('FGP TV prox with objective equal to {:.2f}'.format(valObjFGP[0])) +data_energy = 0.5*np.sum(np.power((xtv_fgp.as_array() - y.array),2)) +EnergytotalFGP = data_energy + 0.5*valObjFGP[0] +plt.title('FGP TV prox with objective equal to {:.2f}'.format(EnergytotalFGP)) plt.show() -print(valObjFGP[0]) +print(EnergytotalFGP) #%% Split-Bregman-TV regularisation -g_sb = _SB_TV_(lambdaReg = lam_tv,iterationsTV=150,tolerance=0,methodTV=0,printing=0,device='cpu') +g_sb = _SB_TV_(lambdaReg = lam_tv,iterationsTV=1000,tolerance=0,methodTV=0,printing=0,device='cpu') xtv_sb = g_sb.prox(y,1.0) @@ -141,9 +142,11 @@ 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])) +data_energy = 0.5*np.sum(np.power((xtv_sb.as_array() - y.array),2)) +EnergytotalSB = data_energy + 0.5*valObjSB[0] +plt.title('SB TV prox with objective equal to {:.2f}'.format(EnergytotalSB)) plt.show() -print(valObjSB[0]) +print(EnergytotalSB) #%% |