def _main():
# Create phantom
l = load_lena()
ph = portal.utils.misc.phantom_mask(l)
# Create Projector and Backprojector
npx = l.shape[0]
nangles = 80
AST = portal.operators.tomography.AstraToolbox(npx, nangles)
# Configure the TV regularization
Lambda = 5.0
# Configure the optimization algorithm (Chambolle-Pock for TV min)
K = lambda x : AST.proj(x)
Kadj = lambda x : AST.backproj(x, filt=True)
n_it = 101
# Run the algorithm to reconstruct the sinogram
sino = K(ph)
en, res = portal.algorithms.chambollepock.chambolle_pock_tv(sino, K, Kadj, Lambda, L=22.5, n_it=301, return_all=True)
# Display the result, compare to FBP
res_fbp = Kadj(sino)
portal.utils.misc.my_imshow((res_fbp, res), (1,2), cmap="gray", nocbar=True)
scipy.misc.imsave("lena_tomo_fbp.png", res_fbp)
scipy.misc.imsave("lena_tomo_tv.png", res)
def _main(CASE, DO_RETURN_ALL):
l = load_lena()
SAVE = False
if CASE == 1: # Denoising (Gaussian noise)
pc = 0.3
lb = l + np.random.rand(l.shape[0], l.shape[1])*l.max() * pc
Lambda = 17.
# With custom C-P
#~ res = chambolle_pock(lb, Lambda, 101)
Id = lambda x : x
K = Id
Kadj = Id
res = portal.algorithms.chambollepock.chambolle_pock_tv(lb, K, Kadj, Lambda, L= 3.5, n_it=151, return_all=DO_RETURN_ALL)
if DO_RETURN_ALL: res = res[1]
portal.utils.misc.my_imshow([lb, res], shape=(1,2), cmap="gray")
if SAVE:
scipy.misc.imsave("lena_gaussian_noise.png", lb)
scipy.misc.imsave("lena_gaussian_noise_cp.png", res)
if CASE == 2: # deblurring (Gaussian blur)
sigma = 2.6
# Define the operator A and its adjoint
gaussian_kernel = portal.utils.misc.gaussian1D(sigma)
Blur = portal.operators.convolution.ConvolutionOperator(gaussian_kernel)
A = lambda x : Blur*x
Aadj = lambda x : Blur.adjoint() * x
# Create the blurred image
lb = A(l)
Lambda = 0.03
res = portal.algorithms.chambollepock.chambolle_pock_tv(lb, A, Aadj, Lambda, n_it=801, return_all=DO_RETURN_ALL)
if DO_RETURN_ALL: res = res[1]
portal.utils.misc.my_imshow([lb, res], shape=(1,2), cmap="gray")
if SAVE:
scipy.misc.imsave("lena_gaussian_blur.png", lb)
scipy.misc.imsave("lena_gaussian_blur_cp.png", res)
if CASE == 3: # denoising (salt & pepper noise)
lb = sp_noise(l, 0.1, 0.1)
Lambda = 0.7
Id = lambda x : x
A = Id
Aadj = Id
res = portal.algorithms.chambollepock.chambolle_pock_l1_tv(lb, A, Aadj, Lambda, n_it=1001, return_all=DO_RETURN_ALL)
if DO_RETURN_ALL: res = res[1]
portal.utils.misc.my_imshow([lb, res], shape=(1,2), cmap="gray")
if SAVE:
scipy.misc.imsave("lena_sp_noise.png", lb)
scipy.misc.imsave("lena_sp_noise_cp.png", res)
# Here many iterations are required.
# Warning : isotropic TV is zero at the beginning ; this is an entirely non-smooth problem !
if CASE == 4: # tomographic reconstruction with TV-L1
n_proj = 80
tomo = portal.operators.tomography.AstraToolbox(l.shape[0], n_proj)
A = lambda x : tomo.proj(x)
Aadj = lambda x : tomo.backproj(x, filt=True)
portal.operators.tomography.clipCircle(l)
sino = A(l) * 1.5708/n_proj
Lambda = 0.1
res = portal.algorithms.chambollepock.chambolle_pock_tv(sino, A, Aadj, Lambda, L=2.5e1, n_it=301, return_all=DO_RETURN_ALL)
if DO_RETURN_ALL: res = res[1]
fbp = Aadj(sino)
#
portal.operators.tomography.clipCircle(fbp)
portal.operators.tomography.clipCircle(res)
#
portal.utils.misc.my_imshow([fbp, res], shape=(1,2), cmap="gray")
def bin2(im):
return (im[::2, ::2] + im[1::2, ::2] + im[::2, 1::2] + im[1::2, 1::2])/2.
def antibin2(im):
res = np.zeros((2*im.shape[0], 2*im.shape[1]))
res[::2, ::2] = im
res[::2, 1::2] = im
res[1::2, ::2] = im
res[1::2, 1::2] = im
return res/2.
if __name__ == '__main__':
l = load_lena()
# 2-zoom
#~ K = bin2
#~ Kadj = antibin2
# 4-zoom
K = lambda x : bin2(bin2(x))
Kadj = lambda x : antibin2(antibin2(x))
lb = K(l) # Use lb = l instead of lb = K(l) for "true" digital zooming of lena (slower !)
'''
# TV-zooming
#------------
Lambda = 0.5