add FISTA demos

2 files changed, 241 insertions, 0 deletions

diff --git a/Wrappers/Python/wip/Demos/FISTA_vs_CGLS.py b/Wrappers/Python/wip/Demos/FISTA_vs_CGLS.py
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+++ b/Wrappers/Python/wip/Demos/FISTA_vs_CGLS.py
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+# -*- coding: utf-8 -*-
+#   This work is part of the Core Imaging Library developed by
+#   Visual Analytics and Imaging System Group of the Science Technology
+#   Facilities Council, STFC
+
+#   Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca
+
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+
+#       http://www.apache.org/licenses/LICENSE-2.0
+
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, AcquisitionData
+
+import numpy as np 
+import numpy                          
+import matplotlib.pyplot as plt
+
+from ccpi.optimisation.algorithms import FISTA, CGLS
+
+from ccpi.optimisation.operators import Gradient
+from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, FunctionOperatorComposition
+from skimage.util import random_noise
+from ccpi.astra.ops import AstraProjectorSimple
+            
+#%%
+
+N = 75
+x = np.zeros((N,N))
+x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5
+x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1
+
+data = ImageData(x)
+ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N)
+
+detectors = N
+angles = np.linspace(0, np.pi, N, dtype=np.float32)
+
+ag = AcquisitionGeometry('parallel','2D',angles, detectors)
+Aop = AstraProjectorSimple(ig, ag, 'gpu')
+sin = Aop.direct(data)
+
+# Create noisy data. Apply Gaussian noise
+
+np.random.seed(10)
+noisy_data = sin + AcquisitionData(np.random.normal(0, 3, sin.shape))
+
+# Regularisation Parameter
+
+operator = Gradient(ig)
+
+fidelity = FunctionOperatorComposition(L2NormSquared(b=noisy_data), Aop)
+regularizer = ZeroFunction()
+
+x_init = ig.allocate()
+
+## Setup and run the FISTA algorithm
+opt = {'tol': 1e-4, 'memopt':True}
+fista = FISTA(x_init=x_init , f=fidelity, g=regularizer, opt=opt)
+fista.max_iteration = 20
+fista.update_objective_interval = 1
+fista.run(20, verbose=True)
+
+## Setup and run the CGLS algorithm        
+cgls = CGLS(x_init=x_init, operator=Aop, data=noisy_data)
+cgls.max_iteration = 20
+cgls.run(20, verbose=True)
+
+diff = np.abs(fista.get_output().as_array() - cgls.get_output().as_array())
+
+plt.figure(figsize=(15,15))
+plt.subplot(3,1,1)
+plt.imshow(fista.get_output().as_array())
+plt.title('FISTA reconstruction')
+plt.colorbar()
+plt.subplot(3,1,2)
+plt.imshow(cgls.get_output().as_array())
+plt.title('CGLS reconstruction')
+plt.colorbar()
+plt.subplot(3,1,3)
+plt.imshow(diff)
+plt.title('Difference reconstruction')
+plt.colorbar()
+plt.show()
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diff --git a/Wrappers/Python/wip/Demos/FISTA_vs_PDHG.py b/Wrappers/Python/wip/Demos/FISTA_vs_PDHG.py
new file mode 100644
index 0000000..b7777ef
--- /dev/null
+++ b/Wrappers/Python/wip/Demos/FISTA_vs_PDHG.py
@@ -0,0 +1,120 @@
+# -*- coding: utf-8 -*-
+#   This work is part of the Core Imaging Library developed by
+#   Visual Analytics and Imaging System Group of the Science Technology
+#   Facilities Council, STFC
+
+#   Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca
+
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+
+#       http://www.apache.org/licenses/LICENSE-2.0
+
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+from ccpi.framework import ImageData, ImageGeometry
+
+import numpy as np 
+import numpy                          
+import matplotlib.pyplot as plt
+
+from ccpi.optimisation.algorithms import FISTA, PDHG
+
+from ccpi.optimisation.operators import BlockOperator, Gradient, Identity
+from ccpi.optimisation.functions import L2NormSquared, L1Norm, \
+                                        MixedL21Norm, FunctionOperatorComposition, BlockFunction, ZeroFunction
+                                                
+from skimage.util import random_noise
+
+# Create phantom for TV Gaussian denoising
+N = 100
+
+data = np.zeros((N,N))
+data[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5
+data[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1
+data = ImageData(data)
+ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N)
+ag = ig
+
+# Create noisy data. Add Gaussian noise
+n1 = random_noise(data.as_array(), mode = 's&p', salt_vs_pepper = 0.9, amount=0.2)
+noisy_data = ImageData(n1)
+
+# Regularisation Parameter
+alpha = 5
+
+operator = Gradient(ig)
+
+fidelity = L1Norm(b=noisy_data)
+regulariser = FunctionOperatorComposition(alpha * L2NormSquared(), operator)
+
+x_init = ig.allocate()
+
+## Setup and run the PDHG algorithm
+opt = {'tol': 1e-4, 'memopt':True}
+fista = FISTA(x_init=x_init , f=regulariser, g=fidelity, opt=opt)
+fista.max_iteration = 2000
+fista.update_objective_interval = 50
+fista.run(2000, verbose=True)
+
+plt.figure(figsize=(15,15))
+plt.subplot(3,1,1)
+plt.imshow(data.as_array())
+plt.title('Ground Truth')
+plt.colorbar()
+plt.subplot(3,1,2)
+plt.imshow(noisy_data.as_array())
+plt.title('Noisy Data')
+plt.colorbar()
+plt.subplot(3,1,3)
+plt.imshow(fista.get_output().as_array())
+plt.title('TV Reconstruction')
+plt.colorbar()
+plt.show()
+
+# Compare with PDHG
+# Create operators
+op1 = Gradient(ig)
+op2 = Identity(ig, ag)
+
+# Create BlockOperator
+operator = BlockOperator(op1, op2, shape=(2,1) )   
+f = BlockFunction(alpha * L2NormSquared(), fidelity)                                        
+g = ZeroFunction()
+  
+## Compute operator Norm
+normK = operator.norm()
+#
+## Primal & dual stepsizes
+sigma = 1
+tau = 1/(sigma*normK**2)
+#
+#
+## Setup and run the PDHG algorithm
+pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True)
+pdhg.max_iteration = 2000
+pdhg.update_objective_interval = 50
+pdhg.run(2000)
+#
+#%%
+plt.figure(figsize=(15,15))
+plt.subplot(3,1,1)
+plt.imshow(fista.get_output().as_array())
+plt.title('FISTA')
+plt.colorbar()
+plt.subplot(3,1,2)
+plt.imshow(pdhg.get_output().as_array())
+plt.title('PDHG')
+plt.colorbar()
+plt.subplot(3,1,3)
+plt.imshow(np.abs(pdhg.get_output().as_array()-fista.get_output().as_array()))
+plt.title('Diff FISTA-PDHG')
+plt.colorbar()
+plt.show()
+
+