selective=None,
x=x,
ignore_residual=ignore,
disp=disp,
maxiter=maxiter)
x_ri = proximal_GD(y,
forward_fun=uft.forward_ortho,
inverse_fun=uft.inverse_ortho,
sparsify=sparsify,
unsparsify=unsparsify,
reorder_fun=None,
alpha=alpha,
thresh_sep=True,
selective=None,
x=x,
ignore_residual=ignore,
disp=disp,
maxiter=maxiter)
xabs = np.abs(x)
x_cabs = np.abs(x_c)
x_riabs = np.abs(x_ri)
print('Thresholding complex:')
print(' MSE: %g' % compare_mse(xabs, x_cabs))
print(' SSIM: %g' % compare_ssim(xabs, x_cabs))
print('Thresholding real/imag separately:')
print(' MSE: %g' % compare_mse(xabs, x_riabs))
print(' SSIM: %g' % compare_ssim(xabs, x_riabs))
view(np.stack((x, uft.inverse_ortho(y), x_c, x_ri)))
if __name__ == '__main__':
# Same binary smiley face example
do_reordering = True
N = 1000
x = binary_smiley(N)
k = np.sum(np.abs(np.diff(x)) > 0)
np.random.seed(5)
samp = cartesian_pe(x.shape, undersample=.2, reflines=5)
uft = UFT(samp)
# Make the complex measurement in kspace
# Note this is different than uft.forward, as fftshift must be performed
y = uft.forward_ortho(x)
# Solve inverse problem using gradient descent with TV sparsity constraint
x_hat = GD_TV(y,
forward_fun=uft.forward_ortho,
inverse_fun=uft.inverse_ortho,
alpha=.5,
lam=.022,
do_reordering=do_reordering,
x=x,
ignore_residual=True,
disp=True,
maxiter=50)
# Look at the before/after shots
view(np.stack((uft.inverse_ortho(y), x_hat)))
do_reordering = True
N = 1000
x = binary_smiley(N)
k = np.sum(np.abs(np.diff(x)) > 0)
np.random.seed(5)
samp = cartesian_pe(x.shape, undersample=.01, reflines=5)
uft = UFT(samp) # acquisiton model
# Show sampling pattern
plt.imshow(samp, cmap='gray')
plt.title('Sampling Pattern')
plt.show()
# Simulate acquisiton
kspace_u = uft.forward_ortho(x)
imspace_u = uft.inverse_ortho(kspace_u)
# Look at the aliased acquired signal
plt.imshow(np.abs(imspace_u), cmap='gray')
plt.title('Acquired')
plt.show()
# Do IHT, enforcing sparsity in finite differences domain
x_hat = IHT_TV(kspace_u,
forward_fun=uft.forward_ortho,
inverse_fun=lambda x: np.abs(uft.inverse_ortho(x)),
k=k,
mu=1,
tol=1e-8,
do_reordering=do_reordering,
x=x,