diff options
Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Python/demos/PDHG_Tikhonov_Tomo2D.py | 120 |
1 files changed, 83 insertions, 37 deletions
diff --git a/Wrappers/Python/demos/PDHG_Tikhonov_Tomo2D.py b/Wrappers/Python/demos/PDHG_Tikhonov_Tomo2D.py index f17c4fe..63f8955 100644 --- a/Wrappers/Python/demos/PDHG_Tikhonov_Tomo2D.py +++ b/Wrappers/Python/demos/PDHG_Tikhonov_Tomo2D.py @@ -1,21 +1,42 @@ -# -*- 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 2019 Science Technology Facilities Council +# Copyright 2019 University of Manchester +# +# This work is part of the Core Imaging Library developed by Science Technology +# Facilities Council and University of Manchester +# +# 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.txt +# +# 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. +# +#========================================================================= + +""" + +Total Variation Denoising using PDHG algorithm: + +Problem: min_x, x>0 \alpha * ||\nabla x||_{2}^{2} + int A x -g log(Ax + \eta) + + \nabla: Gradient operator + + A: Projection Matrix + g: Noisy sinogram corrupted with Poisson Noise + + \eta: Background Noise + \alpha: Regularization parameter + + + +""" -# 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 @@ -26,33 +47,59 @@ import matplotlib.pyplot as plt from ccpi.optimisation.algorithms import PDHG from ccpi.optimisation.operators import BlockOperator, Gradient -from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, BlockFunction +from ccpi.optimisation.functions import IndicatorBox, L2NormSquared, BlockFunction from skimage.util import random_noise from ccpi.astra.ops import AstraProjectorSimple -# Create phantom for TV 2D tomography -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 +loader = TestData(data_dir=os.path.join(sys.prefix, 'share','ccpi')) + +# Load Data +N = 100 +M = 100 +data = loader.load(TestData.SIMPLE_PHANTOM_2D, size=(N,M), scale=(0,1)) + +ig = data.geometry +ag = ig -data = ImageData(x) -ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) +#Create Acquisition Data and apply poisson noise detectors = N -angles = np.linspace(0, np.pi, N, dtype=np.float32) +angles = np.linspace(0, np.pi, N) ag = AcquisitionGeometry('parallel','2D',angles, detectors) -Aop = AstraProjectorSimple(ig, ag, 'gpu') + +device = input('Available device: GPU==1 / CPU==0 ') + +if device=='1': + dev = 'gpu' +else: + dev = 'cpu' + +Aop = AstraProjectorSimple(ig, ag, 'cpu') sin = Aop.direct(data) -# Create noisy data. Apply Gaussian noise +# Create noisy data. Apply Poisson noise +scale = 0.5 +eta = 0 +n1 = scale * np.random.poisson(eta + sin.as_array()/scale) + +noisy_data = AcquisitionData(n1, ag) -np.random.seed(10) -noisy_data = sin + AcquisitionData(np.random.normal(0, 3, sin.shape)) +# Show Ground Truth and Noisy Data +plt.figure(figsize=(10,10)) +plt.subplot(2,1,1) +plt.imshow(data.as_array()) +plt.title('Ground Truth') +plt.colorbar() +plt.subplot(2,1,2) +plt.imshow(noisy_data.as_array()) +plt.title('Noisy Data') +plt.colorbar() +plt.show() +#%% # Regularisation Parameter -alpha = 500 +alpha = 100 # Create operators op1 = Gradient(ig) @@ -67,7 +114,7 @@ f1 = alpha * L2NormSquared() f2 = 0.5 * L2NormSquared(b=noisy_data) f = BlockFunction(f1, f2) -g = ZeroFunction() +g = IndicatorBox(lower=0) # Compute operator Norm normK = operator.norm() @@ -79,11 +126,10 @@ 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 = 5000 -pdhg.update_objective_interval = 50 +pdhg.max_iteration = 2000 +pdhg.update_objective_interval = 500 pdhg.run(2000) -#%% plt.figure(figsize=(15,15)) plt.subplot(3,1,1) plt.imshow(data.as_array()) @@ -99,8 +145,8 @@ plt.title('Tikhonov Reconstruction') plt.colorbar() plt.show() ## -plt.plot(np.linspace(0,N,N), data.as_array()[int(N/2),:], label = 'GTruth') -plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'Tikhonov reconstruction') +plt.plot(np.linspace(0,N,M), data.as_array()[int(N/2),:], label = 'GTruth') +plt.plot(np.linspace(0,N,M), pdhg.get_output().as_array()[int(N/2),:], label = 'Tikhonov reconstruction') plt.legend() plt.title('Middle Line Profiles') plt.show() |