diff options
Diffstat (limited to 'Wrappers')
| -rwxr-xr-x | Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Denoising.py | 187 |
1 files changed, 96 insertions, 91 deletions
diff --git a/Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Denoising.py b/Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Denoising.py index 0f1effa..c472f36 100755 --- a/Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Denoising.py +++ b/Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Denoising.py @@ -24,15 +24,18 @@ Total Variation Denoising using PDHG algorithm: -Problem: min_x, x>0 \alpha * ||\nabla x||_{2,1} + ||x-g||_{1} +Problem: min_{u}, \alpha * ||\nabla u||_{2,1} + Fidelity(u, g) \alpha: Regularization parameter \nabla: Gradient operator - g: Noisy Data with Salt & Pepper Noise - - + g: Noisy Data + + Fidelity = 1) L2NormSquarred ( \frac{1}{2} * || u - g ||_{2}^{2} ) if Noise is Gaussian + 2) L1Norm ( ||u - g||_{1} )if Noise is Salt & Pepper + 3) Kullback Leibler (\int u - g * log(u) + Id_{u>0}) if Noise is Poisson + Method = 0 ( PDHG - split ) : K = [ \nabla, Identity] @@ -40,10 +43,14 @@ Problem: min_x, x>0 \alpha * ||\nabla x||_{2,1} + ||x-g||_{1} Method = 1 (PDHG - explicit ): K = \nabla + Default: ROF denoising + noise = Gaussian + Fidelity = L2NormSquarred + method = 0 + + """ -from ccpi.framework import ImageData, ImageGeometry - import numpy as np import numpy import matplotlib.pyplot as plt @@ -65,7 +72,7 @@ else: if len(sys.argv) > 1: which_noise = int(sys.argv[1]) else: - which_noise = 0 + which_noise = 1 print ("Applying {} noise") if len(sys.argv) > 2: @@ -73,32 +80,27 @@ if len(sys.argv) > 2: else: method = '0' print ("method ", method) -# Create phantom for TV Salt & Pepper denoising -N = 100 + loader = TestData(data_dir=os.path.join(sys.prefix, 'share','ccpi')) -data = loader.load(TestData.SIMPLE_PHANTOM_2D, size=(N,N)) -data = loader.load(TestData.PEPPERS, size=(N,N)) +data = loader.load(TestData.SHAPES) ig = data.geometry ag = ig # Create noisy data. -# Apply Salt & Pepper noise -# gaussian -# poisson noises = ['gaussian', 'poisson', 's&p'] noise = noises[which_noise] if noise == 's&p': n1 = random_noise(data.as_array(), mode = noise, salt_vs_pepper = 0.9, amount=0.2) elif noise == 'poisson': - n1 = random_noise(data.as_array(), mode = noise, seed = 10) + scale = 5 + n1 = random_noise( data.as_array()/scale, mode = noise, seed = 10)*scale elif noise == 'gaussian': n1 = random_noise(data.as_array(), mode = noise, seed = 10) else: raise ValueError('Unsupported Noise ', noise) noisy_data = ig.allocate() noisy_data.fill(n1) -#noisy_data = ImageData(n1) # Show Ground Truth and Noisy Data plt.figure(figsize=(10,5)) @@ -112,8 +114,14 @@ plt.title('Noisy Data') plt.colorbar() plt.show() -# Regularisation Parameter -alpha = .2 + +# Regularisation Parameter depending on the noise distribution +if noise == 's&p': + alpha = 0.8 +elif noise == 'poisson': + alpha = 1 +elif noise == 'gaussian': + alpha = .3 # fidelity if noise == 's&p': @@ -121,30 +129,25 @@ if noise == 's&p': elif noise == 'poisson': f2 = KullbackLeibler(noisy_data) elif noise == 'gaussian': - f2 = L2NormSquared(b=noisy_data) + f2 = 0.5 * L2NormSquared(b=noisy_data) if method == '0': # Create operators - op1 = Gradient(ig, correlation=Gradient.CORRELATION_SPACECHANNEL) + op1 = Gradient(ig, correlation=Gradient.CORRELATION_SPACE) op2 = Identity(ig, ag) # Create BlockOperator operator = BlockOperator(op1, op2, shape=(2,1) ) # Create functions - f1 = alpha * MixedL21Norm() - #f2 = L1Norm(b = noisy_data) - f = BlockFunction(f1, f2) - + f = BlockFunction(alpha * MixedL21Norm(), f2) g = ZeroFunction() else: - # Without the "Block Framework" operator = Gradient(ig) f = alpha * MixedL21Norm() - #g = L1Norm(b = noisy_data) g = f2 @@ -154,14 +157,15 @@ normK = operator.norm() # Primal & dual stepsizes sigma = 1 tau = 1/(sigma*normK**2) -opt = {'niter':2000, 'memopt': True} + # Setup and run the PDHG algorithm -pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True) +pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma) pdhg.max_iteration = 2000 -pdhg.update_objective_interval = 50 +pdhg.update_objective_interval = 100 pdhg.run(2000) + if data.geometry.channels > 1: plt.figure(figsize=(20,15)) for row in range(data.geometry.channels): @@ -179,11 +183,12 @@ if data.geometry.channels > 1: plt.title('TV Reconstruction') plt.colorbar() plt.subplot(3,4,4+row*4) - plt.plot(np.linspace(0,N,N), data.subset(channel=row).as_array()[int(N/2),:], label = 'GTruth') - plt.plot(np.linspace(0,N,N), pdhg.get_output().subset(channel=row).as_array()[int(N/2),:], label = 'TV reconstruction') + plt.plot(np.linspace(0,ig.shape[1],ig.shape[1]), data.subset(channel=row).as_array()[int(N/2),:], label = 'GTruth') + plt.plot(np.linspace(0,ig.shape[1],ig.shape[1]), pdhg.get_output().subset(channel=row).as_array()[int(N/2),:], label = 'TV reconstruction') plt.legend() plt.title('Middle Line Profiles') plt.show() + else: plt.figure(figsize=(20,5)) plt.subplot(1,4,1) @@ -199,68 +204,68 @@ else: plt.title('TV Reconstruction') plt.colorbar() plt.subplot(1,4,4) - 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 = 'TV reconstruction') + plt.plot(np.linspace(0,ig.shape[1],ig.shape[1]), data.as_array()[int(ig.shape[0]/2),:], label = 'GTruth') + plt.plot(np.linspace(0,ig.shape[1],ig.shape[1]), pdhg.get_output().as_array()[int(ig.shape[0]/2),:], label = 'TV reconstruction') plt.legend() plt.title('Middle Line Profiles') plt.show() -##%% Check with CVX solution - -from ccpi.optimisation.operators import SparseFiniteDiff - -try: - from cvxpy import * - cvx_not_installable = True -except ImportError: - cvx_not_installable = False - - -if cvx_not_installable: - - ##Construct problem - u = Variable(ig.shape) - - DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann') - DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann') - - # Define Total Variation as a regulariser - regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u), DY.matrix() * vec(u)]), 2, axis = 0)) - fidelity = pnorm( u - noisy_data.as_array(),1) - - # choose solver - if 'MOSEK' in installed_solvers(): - solver = MOSEK - else: - solver = SCS - - obj = Minimize( regulariser + fidelity) - prob = Problem(obj) - result = prob.solve(verbose = True, solver = solver) - - diff_cvx = numpy.abs( pdhg.get_output().as_array() - u.value ) - - plt.figure(figsize=(15,15)) - plt.subplot(3,1,1) - plt.imshow(pdhg.get_output().as_array()) - plt.title('PDHG solution') - plt.colorbar() - plt.subplot(3,1,2) - plt.imshow(u.value) - plt.title('CVX solution') - plt.colorbar() - plt.subplot(3,1,3) - plt.imshow(diff_cvx) - plt.title('Difference') - plt.colorbar() - plt.show() - - plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'PDHG') - plt.plot(np.linspace(0,N,N), u.value[int(N/2),:], label = 'CVX') - plt.legend() - plt.title('Middle Line Profiles') - plt.show() - - print('Primal Objective (CVX) {} '.format(obj.value)) - print('Primal Objective (PDHG) {} '.format(pdhg.objective[-1][0])) +###%% Check with CVX solution +# +#from ccpi.optimisation.operators import SparseFiniteDiff +# +#try: +# from cvxpy import * +# cvx_not_installable = True +#except ImportError: +# cvx_not_installable = False +# +# +#if cvx_not_installable: +# +# ##Construct problem +# u = Variable(ig.shape) +# +# DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann') +# DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann') +# +# # Define Total Variation as a regulariser +# regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u), DY.matrix() * vec(u)]), 2, axis = 0)) +# fidelity = pnorm( u - noisy_data.as_array(),1) +# +# # choose solver +# if 'MOSEK' in installed_solvers(): +# solver = MOSEK +# else: +# solver = SCS +# +# obj = Minimize( regulariser + fidelity) +# prob = Problem(obj) +# result = prob.solve(verbose = True, solver = solver) +# +# diff_cvx = numpy.abs( pdhg.get_output().as_array() - u.value ) +# +# plt.figure(figsize=(15,15)) +# plt.subplot(3,1,1) +# plt.imshow(pdhg.get_output().as_array()) +# plt.title('PDHG solution') +# plt.colorbar() +# plt.subplot(3,1,2) +# plt.imshow(u.value) +# plt.title('CVX solution') +# plt.colorbar() +# plt.subplot(3,1,3) +# plt.imshow(diff_cvx) +# plt.title('Difference') +# plt.colorbar() +# plt.show() +# +# plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'PDHG') +# plt.plot(np.linspace(0,N,N), u.value[int(N/2),:], label = 'CVX') +# plt.legend() +# plt.title('Middle Line Profiles') +# plt.show() +# +# print('Primal Objective (CVX) {} '.format(obj.value)) +# print('Primal Objective (PDHG) {} '.format(pdhg.objective[-1][0])) |