def wf(y, mask, iter, verbose=False):
m = y.size
n = mask.sum()
mu_max = 0.2
t0 = 330.
y = y**2
lambd2 = y.sum()
x = np.random.rand(*y.shape)
x[~mask] = np.zeros(m - n)
x = x / np.linalg.norm(x, 2)
for i in range(1000):
x = tools.ifft2d(y * tools.fft2d(x))
x[~mask] = np.zeros(m - n)
x = x / np.linalg.norm(x, 2)
x = x * np.sqrt(lambd2)
for t in range(iter):
mu = np.min([1 - np.exp(-t / t0), mu_max])
f = tools.fft2d(x)
x = x - mu * tools.ifft2d((f**2 - y) * f) / lambd2
x[~mask] = np.zeros(m - n)
x = np.real(x)
res = np.real(x)
return res
def oss(y, mask, beta=0.9, verbose=False):
phase = np.random.rand(*y.shape) * 2 * np.pi
x_prev = np.real(tools.ifft2d(y * np.exp(1j * phase)))
sigma1 = np.linspace(y.shape[0], 1. / y.shape[0], 10)
sigma2 = np.linspace(y.shape[1], 1. / y.shape[1], 10)
filtercount = 10
iter = 2000
Rsize = y.shape[0]
X = np.arange(1, iter + 1)
FX = (filtercount + 1 - np.ceil(X * filtercount / iter)) * np.ceil(
iter / (1 * filtercount))
FX = ((FX - np.ceil(iter / filtercount)) *
(2 * Rsize) / np.max(FX)) + (2 * Rsize / 10)
for i in range(iter):
if i == 0 or FX[i - 1] != FX[i]:
sigma = (FX[i], FX[i])
filter = tools.get_gauss2d(y.shape, sigma, normalize=False)
filter = ifftshift(filter)
Fx_curr = y * np.exp(1j * phase)
x_curr = np.real(tools.ifft2d(Fx_curr))
idx = (x_curr < 0) + (mask == False)
x_curr[idx] = x_prev[idx] - beta * x_curr[idx]
Fx_curr = tools.fft2d(x_curr)
idx = (mask == False)
x_curr[idx] = np.real(tools.ifft2d(Fx_curr * filter))[idx]
Fx_curr = tools.fft2d(x_curr)
phase = np.angle(Fx_curr)
x_prev = x_curr.copy()
return x_curr
def prox(self, x, tau=1):
# z = np.zeros(y.shape)
# z[self.mask] = x.flatten()
z = x
zf = tools.fft2d(z)
mag = 1 / (self.alpha + tau) * (self.alpha * self.y + tau * np.abs(zf))
res = mag * np.exp(1j * np.angle(zf))
res = np.real(tools.ifft2d(res))
# return res[self.mask].reshape(x.shape)
return res
def hio(y, mask, iter, beta=0.9, verbose=False):
phase = np.random.rand(*y.shape) * 2 * np.pi
x_prev = np.real(tools.ifft2d(y * np.exp(1j * phase)))
for i in range(iter):
if verbose:
print('Iteration #{:d} '.format(i + 1), end='')
Fx_curr = y * np.exp(1j * phase)
x_curr = np.real(tools.ifft2d(Fx_curr))
idx = ((1 + beta) * x_curr < x_prev) + (mask == False)
# idx = (x_curr < 0) + (mask == False)
x_curr[idx] = x_prev[idx] - beta * x_curr[idx]
Fx_curr = tools.fft2d(x_curr)
phase = np.angle(Fx_curr)
x_prev = x_curr.copy()
# residual = np.power(y - np.abs(new_f), 2).mean()
return x_curr
def prox(self, x, tau=1):
xf = tools.fft2d(x)
mag = 1 / (self.alpha + tau) * (self.alpha * self.y + tau * np.abs(xf))
res = mag * np.exp(1j*np.angle(xf))
res = np.real(tools.ifft2d(res))
return res
def grad(self, x):
f = tools.fft2d(x)
return self.alpha * np.real(tools.ifft2d(f - y * f / np.abs(f)))
res = np.real(tools.ifft2d(res))
return res
y = scipy.io.loadmat('simulated_circle.mat')
y = y['simulated_circle']
y = ifftshift(y) * np.sqrt(y.size)
# y = y * np.sqrt(y.size)
# y = scipy.io.loadmat('15mm_circle_082621.mat')
# y = ifftshift(y) / 10.
# y = scipy.io.loadmat('LUX_082621.mat')
# y = y['For_PR']
# y = y['Pi_spec_sim_abs_PR']
# y = ifftshift(y) * 3.
y = y * args.scale
v00 = np.real(tools.ifft2d(y))
# print(tools.ifft2d(y))
# * np.sqrt(y.size)
mask = np.ones(y.shape, dtype=bool) * False
midpt = (mask.shape[0]//2, mask.shape[1]//2)
mask[midpt[0]-supdim[0]//2:midpt[0]+supdim[0]-supdim[0]//2,
midpt[1]-supdim[1]//2:midpt[1]+supdim[1]-supdim[1]//2] = np.ones(supdim, dtype=bool) * True
v0 = algo.hio(y, mask, args.hioiter)
v0[~mask] = np.zeros(y.size - mask.sum())
fidelity = FourMagMSE(y, 50**2 / (args.lambd**2), mask)
denoiser = dnsr.DnCNN('DnCNN/weights/dncnn50_17.pth')
optimizer = optim.PnPADMMHIO(fidelity, denoiser)
optimizer.init(v0, np.zeros(y.shape))