def K0_transpose(x): # transpose gradient op
x1 = un_flatten_by_channel(x[:, 0], super_shape)
x2 = un_flatten_by_channel(x[:, 1], super_shape)
GradX = np.array([[-1, 1, 0]]).T
GradY = GradX.T
dX = cv2.filter2D(x1, -1, GradX, borderType=cv2.BORDER_REPLICATE)
dY = cv2.filter2D(x2, -1, GradY, borderType=cv2.BORDER_REPLICATE)
return flatten_by_channel(dX + dY)
def grad_transpose(self, v):
#return v[:,0] + v[:,1]
GradX = np.array([[-1, 1, 0]])
GradY = GradX.T
vx = un_flatten_by_channel(v[:, 0], self.shape)
vy = un_flatten_by_channel(v[:, 1], self.shape)
dX = cv2.filter2D(vx, -1, GradX.T, borderType=cv2.BORDER_REPLICATE)
dY = cv2.filter2D(vy, -1, GradY.T, borderType=cv2.BORDER_REPLICATE)
return flatten_by_channel(dX) + flatten_by_channel(dY)
def NLMProx(v):
# tonemap
v_min = v.min()
v_max = x.max()
if ((v_min - v_max) == 0) and v_max == 0:
return v * 0
v = (v - v_min) / (v_max - v_min)
v = v * 255
v = v.astype(np.uint8)
v = un_flatten_by_channel(v, super_shape)
#cv2.imshow("Before NLM", v)
v = cv2.fastNlMeansDenoisingColored(v)
#cv2.imshow("After NLM", v)
#cv2.waitKey(0)
v = flatten_by_channel(v).astype(float) / 255.0
v = v * (v_max - v_min)
v = v + v_min
return v
def K0(x): #gradient
x = un_flatten_by_channel(x, super_shape)
GradX = np.array([[-1, 1, 0]])
GradY = GradX.T
dX = cv2.filter2D(x, -1, GradX, borderType=cv2.BORDER_REPLICATE)
dY = cv2.filter2D(x, -1, GradY, borderType=cv2.BORDER_REPLICATE)
dX = flatten_by_channel(dX)[:, np.newaxis]
dY = flatten_by_channel(dY)[:, np.newaxis]
return np.concatenate((dX, dY), axis=1)
def totalVariation(self, x, y):
x = un_flatten_by_channel(x, self.shape)
dX, dY = self.grad(x)
dX = flatten_by_channel(dX)
dY = flatten_by_channel(dY)
D = np.concatenate((dX, dY))
v = y + (self.gamma*flatten_by_channel(D))
v_orig = v
v = v/self.gamma
v = self.L1_norm_prox(v, 1/self.gamma)
return v_orig - self.gamma*v
def NLM(self, x, y):
v = y + (self.gamma * x)
v_orig = v.copy()
v = v / self.gamma
v = un_flatten_by_channel(v, self.shape)
v_max = v.max()
v_min = v.min()
v = (v - v_min) * 255 / (v_max - v_min)
prox = flatten_by_channel(
cv2.fastNlMeansDenoisingColored(v.astype(
np.uint8))).astype(float) / 255
prox = prox * (v_max - v_min)
prox = prox + v_min
return v_orig - (self.gamma * prox)
def forward(self, z, init_guess, iters):
self.shape = z.shape
x_bar = x = init_guess
# For debugging
answer = flatten_by_channel(z)
z = self.A @ flatten_by_channel(z)
y = 0
print("Starting iterations...")
for _ in range(iters):
y = self.penalty(x_bar, y)
x_new, _ = self.data_fidelity(x, y, z)
x_bar = self.extrapolation(x, x_new)
x = x_new
out = (x - x.min()) * 255 / (x.max() - x.min())
cv2.imshow("intermediate",
un_flatten_by_channel(out.astype(np.uint8), self.shape))
cv2.waitKey(100)
print((((out / 255.0)**2 + (answer**2))**0.5).sum())
return x
def extrapolation(x_new, x_old):
return x_new + (theta * (x_new - x_old))
initial = None
for _ in range(iters):
y = penalty_op()
x_old = x
x, _ = data_fidelity_op()
x_bar = extrapolation(x, x_old)
print(x.min())
print(x.max())
print()
reconstructed_im = un_flatten_by_channel(x, super_shape)
reconstructed_im = (reconstructed_im - reconstructed_im.min()) / (
reconstructed_im.max() - reconstructed_im.min())
reconstructed_im = reconstructed_im * 255
reconstructed_im[reconstructed_im < 0] = 0
reconstructed_im[reconstructed_im > 255] = 254
reconstructed_im = reconstructed_im.astype(np.uint8)
if (initial is None):
initial = reconstructed_im.copy()
cv2.imshow("x", reconstructed_im)
cv2.waitKey(10)
print("Done")
cv2.imshow("x0", initial)
def extrapolation(x_new, x_old):
return x_new + (theta * (x_new - x_old))
initial = None
for _ in range(iters):
y = penalty_op()
x_old = x
x, _ = data_fidelity_op()
x_bar = extrapolation(x, x_old)
print(x.min())
print(x.max())
print()
reconstructed_im = un_flatten_by_channel(x, im.shape)
reconstructed_im = (reconstructed_im - reconstructed_im.min()) / (
reconstructed_im.max() - reconstructed_im.min())
reconstructed_im = reconstructed_im * 255
reconstructed_im[reconstructed_im < 0] = 0
reconstructed_im[reconstructed_im > 255] = 254
reconstructed_im = reconstructed_im.astype(np.uint8)
if (initial is None):
initial = reconstructed_im.copy()
cv2.imshow("x", reconstructed_im)
cv2.waitKey(10)
print("Done")
cv2.imshow("x0", initial)