def _focusing(X, phi, Na, Nr, ftshift=False, isfft=True):
d = X.dim()
sizea, sizer = [1] * d, [1] * d
returns = []
if Na is not None:
pa = phi[0]
sizea[0], sizea[-3], sizea[-1] = pa.size(0), pa.size(1), 2
epa = th.stack((th.cos(pa), -th.sin(pa)), dim=-1)
epa = epa.reshape(sizea)
if isfft:
X = ts.fft(X, axis=-3, shift=ftshift)
X = ts.ebemulcc(X, epa)
X = ts.ifft(X, axis=-3, shift=ftshift)
returns.append(pa.data)
if Nr is not None:
if Na is None:
pr = phi[0]
else:
pr = phi[1]
sizer[0], sizer[-2], sizer[-1] = pr.size(0), pr.size(1), 2
epr = th.stack((th.cos(pr), -th.sin(pr)), dim=-1)
epr = epr.reshape(sizer)
if isfft:
X = ts.fft(X, axis=-2, shift=ftshift)
X = ts.ebemulcc(X, epr)
X = ts.ifft(X, axis=-2, shift=ftshift)
returns.append(pr.data)
return [X] + returns
def phase_correction(x, phi, nsub=None, axis=-2, ftshift=True):
if th.is_complex(x):
# N, Na, Nr = x.size(0), x.size(-2), x.size(-1)
cplxflag = True
elif x.size(-1) == 2:
# N, Na, Nr = x.size(0), x.size(-3), x.size(-2)
x = th.view_as_complex(x)
cplxflag = False
else:
raise TypeError(
'x is complex and should be in complex or real represent formation!'
)
if axis == -2 or axis == 1: # azimuth
axis = -2
if axis == -1 or axis == 2: # range
axis = -1
if x.dim() == 2:
x = x.unsqueeze(0)
if phi.dim() == 1:
phi = phi.unsqueeze(0)
if phi.size(-1) != 1:
phi = phi.unsqueeze(-1)
phi = phi.unsqueeze(-1)
N, Na, Nr = x.size()
Nx = x.size(axis)
pshape = [1, 1, 1]
pshape[0] = N
pshape[axis] = Nx
phi = phi.reshape(pshape)
if nsub is None:
nsub = Nx
# X = ts.fft(SI, axis=axis, shift=ftshift)
# X = X * np.exp(-1j * phi)
# SI = ts.ifft(X, axis=axis, shift=ftshift)
xc = x.clone().detach()
d = xc.dim()
for n in range(0, Nx, nsub):
idx = ts.sl(d, axis, range(n, n + nsub))
xsub = x[idx]
phisub = phi[idx]
Xsub = ts.fft(xsub, axis=axis, shift=ftshift)
xc[idx] = ts.ifft(Xsub * th.exp(-1j * phisub),
axis=axis,
shift=ftshift)
if not cplxflag:
xc = th.view_as_real(xc)
return xc
def focus(self, X, pa=None, pr=None):
# X --> N-1-Na-Nr-2
# pa --> N-Na
# pr --> N-Nr
if pa is None and pr is None:
return X
if pa is not None:
X = ts.fft(X, nfft=None, axis=2, norm=False)
pa = pa.reshape(pa.size(0), 1, int(pa.numel() / pa.size(0)), 1)
epa = th.stack((th.cos(pa), th.sin(pa)), dim=-1)
X = ts.ebemulcc(X, epa)
if pr is not None:
X = ts.fft(X, nfft=None, axis=3, norm=False)
pr = pr.reshape(pr.size(0), 1, 1, int(pr.numel() / pr.size(0)))
epr = th.stack((th.cos(pr), th.sin(pr)), dim=-1)
X = ts.ebemulcc(X, epr)
if pa is not None:
X = ts.ifft(X, nfft=None, axis=2, norm=False)
if pr is not None:
X = ts.ifft(X, nfft=None, axis=3, norm=False)
return X
def af_ffo(g, w=None, p=2, niter=10, eta=0.5, tol=1e-2, ftshift=False):
if p < 1:
raise ValueError('p should be larger than 1!')
G = ts.fft(g, axis=-3, shift=ftshift)
Na, Nr = G.size(-3), G.size(-2)
d = G.dim()
wshape = [1] * d
wshape[-3] = Na
if w is None:
w = th.ones(wshape, device=G.device, dtype=G.dtype)
if eta is None:
eta = ts.PI / 2.
phi0, i = 1e3, 0
phi = th.zeros(wshape, device=G.device, dtype=G.dtype)
diff = th.ones(wshape, device=G.device, dtype=G.dtype)
# print(i, niter, (phi - phi0).sum(), tol)
# print(w.shape)
while (i < niter and (phi - phi0).abs().sum() > tol):
phi0 = phi
while (diff.abs().sum() > tol):
Gabs = th.sqrt(G[..., 0]**2 + G[..., 1]**2).unsqueeze(-1)
# print(Gabs.shape, w.shape)
gamman = th.sum(w * Gabs, axis=-3, keepdim=True)
print(gamman.min(), gamman.max(), "===")
# print(gamman.shape, w.shape, Gabs.shape)
Dphi = (w * p * th.sum((gamman ** (p - 1)) * Gabs, axis=-2, keepdim=True) / 2.) / \
(-w * w * p * (p - 1) * th.sum((gamman ** (p - 2)) * (Gabs ** 2), axis=-2, keepdim=True) / 4.)
# print(Dphi.shape)
diff = eta * Dphi
phi = phi - diff
epa = th.cat((th.cos(phi), th.sin(phi)), axis=-1)
G = ts.ebemulcc(G, epa)
print(Dphi.min(), Dphi.max())
print(diff.min(), diff.max())
print(phi.min(), phi.max())
i += 1
eta /= 2.
print(i, eta)
g = ts.ifft(G, axis=-3, shift=ftshift)
return g
def af_ffo_sm(x,
p=2,
niter=10,
delta=None,
toli=1e-2,
tolo=1e-2,
ftshift=True,
islog=False):
"""Stage-by-Stage minimum entropy
[1] Morrison Jr, Robert Lee; Autofocus, Entropy-based (2002): Entropy-based autofocus for synthetic aperture radar.
Parameters
----------
x : Tensor
Corrupt complex SAR image. N-Na-Nr(complex) or N-Na-Nr-2(real)
niter : int, optional
The number of iteration (the default is 10)
delta : {float or None}, optional
The change step (the default is None (i.e. PI))
toli : int, optional
Tolerance error for inner loop (the default is 1e-2)
tolo : int, optional
Tolerance error for outer loop (the default is 1e-2)
ftshift : bool, optional
Shift the zero frequency to center? (the default is True)
islog : bool, optional
Print log information? (the default is False)
"""
if delta is None:
delta = ts.PI
if th.is_complex(x):
x = th.view_as_real(x)
cplxflag = True
elif x.size(-1) == 2:
cplxflag = False
else:
raise TypeError(
'x is complex and should be in complex or real represent formation!'
)
d = x.dim()
N, Na, Nr = x.size(0), x.size(-3), x.size(-2)
wshape = [1] * d
wshape[-3] = Na
X = ts.fft(x, axis=-3, shift=ftshift)
phio = th.zeros(wshape, device=x.device, dtype=x.dtype)
ephi = th.cat((th.cos(phio), th.sin(phio)), dim=-1)
# print(wshape, phio.min(), phio.max(), ephi.min(), ephi.max())
So = __fdnorm(ts.ebemulcc(X, ephi), p)
ii, io, Soi0, Soo0 = 0, 0, 1e13, 1e13
print(ii, So, Soi0, Soo0)
while (ii < niter):
Soo0 = So
while True:
Soi0 = So
print(ii, "===", d)
for a in range(Na):
phi1, phi2 = phio.clone().detach(), phio.clone().detach()
for n in range(N):
print(n, N, Na, a, delta)
phi1[n, a] += delta
phi2[n, a] -= delta
ephi1 = th.cat((th.cos(phi1), -th.sin(phi1)), dim=-1)
S1 = __fdnorm(ts.ebemulcc(X, ephi1), p)
ephi2 = th.cat((th.cos(phi2), -th.sin(phi2)), dim=-1)
S2 = __fdnorm(ts.ebemulcc(X, ephi2), p)
print(phi1.shape, phi2.shape, ephi1.shape, ephi2.shape, "]]]")
print(N, S1, S2, So, S1 - S2, th.sum(ephi1 - ephi2),
th.sum(phi1 - phi2))
if S1 > So:
So = S1
phio = phi1.clone().detach()
print("111")
elif S2 > So:
print("222")
So = S2
phio = phi2.clone().detach()
print(Soi0, So, S1, S2, abs(So - Soi0), io, "+++")
if abs(So - Soi0) < toli and io > niter:
break
io += 1
print(ii, delta, abs(So - Soo0), tolo,
abs(So - Soo0) < tolo, So, Soo0, "So")
print(ii, delta, abs(So - Soi0), tolo,
abs(So - Soi0) < toli, So, Soi0, "So")
if abs(So - Soo0) > tolo:
ii += 1
delta /= 2.
else:
break
ephi = th.cat((th.cos(phio), th.sin(phio)), dim=-1)
return ts.ifft(ts.ebemulcc(X, ephi)), ephi
def defocus(x, pa=None, pr=None, isfft=True, ftshift=True):
r"""Defocus image with given phase error
Defocus image in azimuth by
.. math::
Y(k, n_r)=\sum_{n_a=0}^{N_a-1} X(n_a, n_r) \exp \left(j \varphi_{n_a}\right) \exp \left(-j \frac{2 \pi}{N_a} k n_a\right)
where, :math:`\varphi_{n_a}` is the estimated azimuth phase error of the :math:`n_a`-th azimuth line, :math:`y(k, n_r)` is
the focused pixel.
The defocus method in range is the same as azimuth.
Args:
x (Tensor): Focused image data :math:`{\mathbf X} \in{\mathbb C}^{N\times N_c\times N_a\times N_r}` or
:math:`{\mathbf X} \in{\mathbb R}^{N\times N_a\times N_r\times 2}` or
:math:`{\mathbf X} \in{\mathbb R}^{N\times N_c\times N_a\times N_r\times 2}`.
pa (Tensor, optional): Defocus parameters in azimuth, phase error in rad unit. (the default is None, not focus)
pr (Tensor, optional): Defocus parameters in range, phase error in rad unit. (the default is None, not focus)
isfft (bool, optional): Is need do fft (the default is True)
ftshift (bool, optional): Is shift zero frequency to center when do fft/ifft/fftfreq (the default is True)
Returns:
(Tensor): A tensor of defocused images.
Raises:
TypeError: :attr:`x` is complex and should be in complex or real represent formation!
"""
if type(x) is not th.Tensor:
x = th.tensor(x)
if th.is_complex(x):
# N, Na, Nr = x.size(0), x.size(-2), x.size(-1)
x = th.view_as_real(x)
crepflag = True
elif x.size(-1) == 2:
# N, Na, Nr = x.size(0), x.size(-3), x.size(-2)
crepflag = False
else:
raise TypeError('x is complex and should be in complex or real represent formation!')
d = x.dim()
sizea, sizer = [1] * d, [1] * d
if pa is not None:
sizea[0], sizea[-3], sizea[-1] = pa.size(0), pa.size(1), 2
epa = th.stack((th.cos(pa), th.sin(pa)), dim=-1)
epa = epa.reshape(sizea)
if isfft:
x = ts.fft(x, axis=-3, shift=ftshift)
x = ts.ebemulcc(x, epa)
x = ts.ifft(x, axis=-3, shift=ftshift)
if pr is not None:
sizer[0], sizer[-2], sizer[-1] = pr.size(0), pr.size(1), 2
epr = th.stack((th.cos(pr), th.sin(pr)), dim=-1)
epr = epr.reshape(sizer)
if isfft:
x = ts.fft(x, axis=-2, shift=ftshift)
x = ts.ebemulcc(x, epr)
x = ts.ifft(x, axis=-2, shift=ftshift)
if crepflag:
x = th.view_as_complex(x)
return x
def imaging(self, Xc):
if self.Na is not None:
Xc = ts.ifft(Xc, axis=-3, shift=self.ftshift)
if self.Nr is not None:
Xc = ts.ifft(Xc, axis=-2, shift=self.ftshift)
return Xc