def blind_lucy_wrapper(image, max_its=8, its=5, N_filter=None, weiner=False,
estimation_noise=0, filter_estimation=1,
observation_noise=0):
f = io.imread('../data/'+image+'.png', dtype=float)
if len(f.shape) == 3: f = f.mean(axis=2)
f /= f.max()
if N_filter:
g = helper.gaussian(sigma=N_filter/3, N=N_filter)
g_k = helper.gaussian(sigma=N_filter/3 * filter_estimation, N=N_filter)
g_0 = g_k.copy()
else:
N = f.shape[0]
sigma = N/40
g = helper.gaussian(sigma=sigma, N=N)
g_k = helper.gaussian(sigma=filter_estimation*sigma, N=N)
c = fftconvolve(f, g, mode='same')
#c += observation_noise*np.random.randn(*c.shape)
#f_k = f + estimation_noise*np.random.randn(*f.shape)
f_k = c.copy()
for k in range(int(max_its)):
f_k1 = f_k.copy()
for i in range(its):
g_k = richardson_lucy(c, f_k1, g_k, iterations=1, clip=False)
f_k = richardson_lucy(c, g_k, f_k, iterations=1, clip=False)
print("on {}, f.max() = {:0.3e}, g.max() = {:0.3e}".format(k, np.abs(f_k.max()), np.abs(g_k.max())))
f_k, g_k = np.abs(f_k), np.abs(g_k)
helper.show_images({'estimation':f_k, 'original':f, 'observations':c,
'kernel estimate':g_k})
return f_k, g_k
def blind_lucy_wrapper(image, max_its=8, its=5, N_filter=3, weiner=False,
estimation_noise=0, filter_estimation=1,
observation_noise=0):
f = io.imread('../data/'+image+'.png', dtype=float)
if len(f.shape) == 3: f = f.mean(axis=2)
f /= f.max()
print(f.shape)
g = helper.gaussian(sigma=N_filter/3, N=N_filter)
g_k = helper.gaussian(sigma=N_filter/3 * filter_estimation, N=N_filter)
g_0 = g_k.copy()
c = fftconvolve(f, g, mode='same')
#c += observation_noise*np.random.randn(*c.shape)
f_k = f + estimation_noise*np.random.randn(*f.shape)
#f_k = c.copy()
for k in range(int(max_its)):
g_k = richardson_lucy(g_k, f_k, iterations=int(its), clip=True)
if weiner: f_k = wiener(f_k, g_k, 1e-5)
else: f_k = richardson_lucy(f_k, g_k, iterations=int(its), clip=True)
print("on {}, f.max() = {:0.3e}, g.max() = {:0.3e}".format(k, np.abs(f_k.max()),
np.abs(g_k.max())))
f_k, g_k = np.abs(f_k), np.abs(g_k)
helper.show_images({'estimation':f_k, 'original':f, 'observations':c})
def show_imgs():
helper.show_images({
'x': x,
'h': h,
'x_k': x_k,
'h_k': np.abs(h_k)
},
colorbar=True,
drawnow=True,
cmap='gray')
def test_pca():
dataloc = "../data/USPS.mat"
A = load_data(dataloc)
ps = [10, 50, 100, 200]
for p in ps:
U_p, A1 = pca(A, p)
Re_A = reconstruction(U_p, A1)
error = reconstruct_error(A, Re_A)
print(error)
show_images(Re_A, p, 1)
show_images(Re_A, p, 2)
def test_pca():
dataloc = "../data/USPS.mat"
A = load_data(dataloc)
ps = [10, 50, 100, 200]
for p in ps:
pca = PCA(A, p)
Ap = pca.get_reduced()
A_re = pca.reconstruction(Ap)
error = reconstruct_error(A, A_re)
print(error)
show_images(A_re, p, 1)
show_images(A_re, p, 2)
import numpy as np
from numpy.fft import ifft2, fft2, fftshift
import helper
output = helper.blur_and_noise(sigma=6, noise_sigma=1e-5)
(x, X), (y, Y), (h, H) = output
H_k = H * 5 # + np.random.randn(*H.shape) * 0.1
iterations = 20
for k in range(iterations):
X_k = Y / (H_k + 1e-9)
H_k = Y / (X_k + 1e-9)
x_k = fftshift(ifft2(X_k))
helper.show_images({'original': x, 'estimate': np.abs(x_k), 'observations': y})
import numpy as np
from skimage import color, data, restoration
import numpy as np
import numpy.random as npr
from scipy.signal import convolve2d
import helper
camera = color.rgb2gray(data.camera())
camera = helper.get_image('cameraman')
from scipy.signal import convolve2d
psf = np.ones((5, 5)) / 25
camera = convolve2d(camera, psf, 'same')
camera += 0.1 * camera.std() * np.random.standard_normal(camera.shape)
deconvolved = restoration.richardson_lucy(camera, psf, 5)
helper.show_images({'deconv':deconvolved})
import numpy as np
from skimage import color, data, restoration
import numpy as np
import numpy.random as npr
from scipy.signal import convolve2d
import helper
camera = color.rgb2gray(data.camera())
camera = helper.get_image('cameraman')
from scipy.signal import convolve2d
psf = np.ones((5, 5)) / 25
camera = convolve2d(camera, psf, 'same')
camera += 0.1 * camera.std() * np.random.standard_normal(camera.shape)
deconvolved = restoration.richardson_lucy(camera, psf, 5)
helper.show_images({'deconv': deconvolved})
if clip:
im_deconv[im_deconv > 1] = 1
im_deconv[im_deconv < -1] = -1
return fft2(im_deconv), im_deconv
f = helper.get_image('cameraman64')
N, _ = f.shape
f /= f.sum()
F = fft2(f)
g = helper.gaussian(sigma=1, N=N)
g /= g.sum()
G = fft2(g)
C = F*G
c = ifft2(C)
max_its = 80
for k in range(int(max_its)):
F, f_k = update_f(F, G, C, eps=0, iterations=1)
#G, g_k = update_g(F, G, C, eps=0, iterations=2)
print("iteration {}, f.max = {:0.2e}, g.max = {:0.2e}".format(k, np.abs(f).max(),
np.abs(g).max()))
G, g_k = update_g(F, G, C, eps=0, iterations=2)
helper.show_images({'kernel':np.abs(g_k), 'estimate':np.abs(f_k),
'observations':np.abs(c)})
# im_deconv *= fftconvolve(relative_blur, psf_mirror, 'same')
if clip:
im_deconv[im_deconv > 1] = 1
im_deconv[im_deconv < -1] = -1
return fft2(im_deconv), im_deconv
f = helper.get_image("cameraman64")
N, _ = f.shape
f /= f.sum()
F = fft2(f)
g = helper.gaussian(sigma=1, N=N)
g /= g.sum()
G = fft2(g)
C = F * G
c = ifft2(C)
max_its = 80
for k in range(int(max_its)):
F, f_k = update_f(F, G, C, eps=0, iterations=1)
# G, g_k = update_g(F, G, C, eps=0, iterations=2)
print("iteration {}, f.max = {:0.2e}, g.max = {:0.2e}".format(k, np.abs(f).max(), np.abs(g).max()))
G, g_k = update_g(F, G, C, eps=0, iterations=2)
helper.show_images({"kernel": np.abs(g_k), "estimate": np.abs(f_k), "observations": np.abs(c)})
import numpy as np
from numpy.fft import ifft2, fft2, fftshift
import helper
output = helper.blur_and_noise(sigma=6, noise_sigma=1e-5)
(x, X), (y, Y), (h, H) = output
H_k = H * 5# + np.random.randn(*H.shape) * 0.1
iterations = 20
for k in range(iterations):
X_k = Y / (H_k + 1e-9)
H_k = Y / (X_k + 1e-9)
x_k = fftshift(ifft2(X_k))
helper.show_images({'original':x, 'estimate':np.abs(x_k), 'observations':y})
