def demo():
image = normalise(camera().astype("f"))
blurred_image, psf_kernel = add_microscope_blur_2d(image)
noisy_blurred_image = add_poisson_gaussian_noise(
blurred_image, alpha=0.001, sigma=0.1, sap=0.01, quant_bits=10
)
deconvolved_image = cg_restoration(
noisy_blurred_image,
kernel=psf_kernel,
num_iterations=200,
lmbda=4.5e-3,
mu=1e-10,
)
deconvolved_image = deconvolved_image.clip(0, 1)
import napari
with napari.gui_qt():
viewer = napari.Viewer()
viewer.add_image(image, name="image")
viewer.add_image(blurred_image, name="blurred")
viewer.add_image(noisy_blurred_image, name="noisy_blurred_image")
viewer.add_image(deconvolved_image, name="deconvolved_image")
def original_demo():
Lambda = 1e-7 # weight of TV regularization
mu = 1e-10 # parameter of TV smoothing
n_it = 500 # number of iterations
image = normalise(camera().astype("f"))
kern = gaussian1D(2.6)
K = ConvolutionOperator(kern)
P = lambda x: K * x
PT = lambda x: K.T() * x
blurred_image = add_noise(P(image), intensity=None, variance=0.00)
en, deconvolved_image = conjugate_gradient_TV(P, PT, blurred_image, Lambda,
mu, n_it)
import matplotlib.pyplot as plt
plt.figure()
plt.plot(en)
plt.show()
import napari
with napari.gui_qt():
viewer = napari.Viewer()
viewer.add_image(image, name="image")
viewer.add_image(blurred_image, name="blurred")
viewer.add_image(deconvolved_image, name="deconvolved_image")
def original_demo():
beta = 1e-7 # weight of TV regularization
n_it = 500 # number of iterations
# Init.
# ------
image = normalise(camera().astype('f'))
kern = gaussian1D(2.6)
K = ConvolutionOperator(kern)
P = lambda x: K * x
PT = lambda x: K.T() * x
# Run
blurred_image = add_noise(P(image), intensity=None, variance=0.00)
L = power_method(P, PT, blurred_image, n_it=200)
print("||K|| = %f" % L)
en, deconvolved_image = chambolle_pock(P, PT, blurred_image, beta, L, n_it)
import matplotlib.pyplot as plt
plt.figure()
plt.plot(en)
plt.show()
import napari
with napari.gui_qt():
viewer = napari.Viewer()
viewer.add_image(image, name='image')
viewer.add_image(blurred_image, name='blurred')
viewer.add_image(deconvolved_image, name='deconvolved_image')
def demo():
image = normalise(camera().astype('f'))
blurred_image, psf_kernel = add_microscope_blur_2d(image)
noisy_blurred_image = add_poisson_gaussian_noise(blurred_image,
alpha=0.001,
sigma=0.1,
sap=0.01,
quant_bits=10)
deconvolved_image = cp_restoration(noisy_blurred_image,
kernel=psf_kernel,
num_iterations=200,
beta=2e-3)
deconvolved_image = deconvolved_image.clip(0, 1)
import napari
with napari.gui_qt():
viewer = napari.Viewer()
viewer.add_image(image, name='image')
viewer.add_image(blurred_image, name='blurred')
viewer.add_image(noisy_blurred_image, name='noisy_blurred_image')
viewer.add_image(deconvolved_image, name='deconvolved_image')