color denoising

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diff --git a/Wrappers/Python/demos/PDHG_examples/PDHG_TV_Color_Denoising.py b/Wrappers/Python/demos/PDHG_examples/PDHG_TV_Color_Denoising.py
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+#========================================================================
+# 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||_{2,1} + ||x-g||_{1}
+             \alpha: Regularization parameter
+             
+             \nabla: Gradient operator 
+             
+             g: Noisy Data with Salt & Pepper Noise
+             
+             
+             Method = 0 ( PDHG - split ) :  K = [ \nabla,
+                                                 Identity]
+                          
+                                                                    
+             Method = 1 (PDHG - explicit ):  K = \nabla    
+             
+             
+"""
+
+import numpy                          
+import matplotlib.pyplot as plt
+
+from ccpi.optimisation.algorithms import PDHG
+
+from ccpi.optimisation.operators import Gradient, BlockOperator, FiniteDiff
+from ccpi.optimisation.functions import MixedL21Norm, MixedL11Norm, L2NormSquared, BlockFunction, L1Norm                      
+from ccpi.framework import TestData, ImageGeometry
+import os, sys
+if int(numpy.version.version.split('.')[1]) > 12:
+    from skimage.util import random_noise
+else:
+    from demoutil import random_noise
+
+loader = TestData(data_dir=os.path.join(sys.prefix, 'share','ccpi'))
+data = loader.load(TestData.PEPPERS, size=(256,256))
+ig = data.geometry
+ag = ig
+
+# Create noisy data. 
+n1 = random_noise(data.as_array(), mode = 'gaussian', var = 0.15, seed = 50)
+noisy_data = ig.allocate()
+noisy_data.fill(n1)
+
+# Show Ground Truth and Noisy Data
+plt.figure(figsize=(10,5))
+plt.subplot(1,2,1)
+plt.imshow(data.as_array())
+plt.title('Ground Truth')
+plt.colorbar()
+plt.subplot(1,2,2)
+plt.imshow(noisy_data.as_array())
+plt.title('Noisy Data')
+plt.colorbar()
+plt.show()
+
+# Regularisation Parameter
+operator = Gradient(ig, correlation=Gradient.CORRELATION_SPACE)
+f1 =  5 * MixedL21Norm()
+g = 0.5 * L2NormSquared(b=noisy_data)
+            
+# Compute operator Norm
+normK = operator.norm()
+
+# Primal & dual stepsizes
+sigma = 1
+tau = 1/(sigma*normK**2)
+
+# Setup and run the PDHG algorithm
+pdhg1 = PDHG(f=f1,g=g,operator=operator, tau=tau, sigma=sigma)
+pdhg1.max_iteration = 2000
+pdhg1.update_objective_interval = 200
+pdhg1.run(1000)
+
+
+# Show results
+plt.figure(figsize=(10,10))
+plt.subplot(2,2,1)
+plt.imshow(data.as_array())
+plt.title('Ground Truth')
+plt.colorbar()
+plt.subplot(2,2,2)
+plt.imshow(noisy_data.as_array())
+plt.title('Noisy Data')
+plt.colorbar()
+plt.subplot(2,2,3)
+plt.imshow(pdhg1.get_output().as_array())
+plt.title('TV Reconstruction')
+plt.colorbar()
+plt.subplot(2,2,4)
+plt.imshow(pdhg2.get_output().as_array())
+plt.title('TV Reconstruction')
+plt.colorbar()
+plt.show()
+