def phi(entry):
# Likelihood fonction
A = MesFonctions.matrice_A(entry[0:size].reshape((NbLigne, NbColonne)), methode="3")
x_ = (
floue.reshape(size) - MesFonctions.conv_matrices(A, entry[size:2 * size].reshape((NbLigne, NbColonne)),
"vecteur",
"coin"))
likelihood = np.dot(np.dot(x_, C_noise_inv), x_.transpose())
# Regularization term
image_ = entry[0:size] # /sum(sum(im))
PSF_ = entry[size:2 * size] # /sum(sum(PSF))
if regularization == "spectral":
fy_ = np.fft(image_)
y_ = 0
for i in range(0, size):
y_ = y_ + i * i * abs(fy_[i]) * abs(fy_[i])
regularization_im = y_
fz_ = np.fft(PSF_)
z_ = 0
for i in range(0, size):
z_ = z_ + i * i * abs(fz_[i]) * abs(fz_[i])
regularization_PSF = z_
elif regularization == "Tikhonov":
y_ = sum(image_ * image_)
regularization_im = math.sqrt(y_)
z_ = sum(PSF_ * PSF_)
regularization_PSF = math.sqrt(z_)
t_ = likelihood + regularization_im + regularization_PSF
return t_
# import Kernel_Estimation_v6 as MonImage
import opkpy_v3
# -----------------------------------------------------------------------------------
# Quelle image importe-t-on ?
# --> "essaie" pour une simple image 9x9
# --> nom de l'image sinon ("test", "chat", etc)
ImageImportee = "essaie"
# -----------------------------------------------------------------------------------
# -----------------------------------------------------------------------------------
# On importe une image
if ImageImportee == "essaie":
NbLigne, NbColonne, image_vraie, latent, kernel_vrai, kernel = MesFonctions.mon_image("create")
floue = MesFonctions.conv(image_vraie, kernel_vrai)
# else :
# NbLigne, NbColonne, image_vraie, kernel_vrai, floue, kernel, inutile = MonImage.Ouverture("ImageImportee")
# -----------------------------------------------------------------------------------
# -----------------------------------------------------------------------------------
# On declare certains parametres
size = NbLigne * NbColonne
regularization = "Tikhonov"
dt = 10
# On fait les conversions en vecteur
floue_v = floue.reshape(size)
kernel_v = kernel.reshape(size)
# On cree la matrice bruit
