def test_11(self):
N = 63
M = 4
Nd = 8
D = np.random.randn(Nd, Nd, M)
X0 = np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
X0[xp] = np.random.randn(X0[xp].size)
S = np.sum(ifftn(
fftn(D, (N, N), (0, 1)) * fftn(X0, None, (0, 1)), None,
(0, 1)).real,
axis=2)
lmbda = 1e-2
L = 1e3
opt = cbpdn.ConvBPDN.Options({
'Verbose': False,
'MaxMainIter': 2000,
'RelStopTol': 1e-9,
'L': L,
'Backtrack': BacktrackStandard()
})
b = cbpdn.ConvBPDN(D, S, lmbda, opt)
b.solve()
X1 = b.X.squeeze()
assert sl.rrs(X0, X1) < 5e-4
Sr = b.reconstruct().squeeze()
assert sl.rrs(S, Sr) < 2e-4
def test_05(self):
N = 16
Nd = 5
K = 2
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N, K)
lmbda = 1e-1
b = cbpdn.ConvBPDN(D, s, lmbda)
assert b.cri.dimC == 0
assert b.cri.dimK == 1
def test_03(self):
N = 16
Nd = 5
Cd = 3
M = 4
D = np.random.randn(Nd, Nd, Cd, M)
s = np.random.randn(N, N, Cd)
lmbda = 1e-1
b = cbpdn.ConvBPDN(D, s, lmbda)
assert b.cri.dimC == 1
assert b.cri.dimK == 0
def test_01(self):
N = 16
Nd = 5
Cs = 3
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N, Cs)
lmbda = 1e-1
b = cbpdn.ConvBPDN(D, s, lmbda, dimK=0)
assert b.cri.dimC == 1
assert b.cri.dimK == 0
def test_02(self):
N = 16
Nd = 5
Cs = 3
K = 5
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N, Cs, K)
lmbda = 1e-1
b = cbpdn.ConvBPDN(D, s, lmbda)
assert b.cri.dimC == 1
assert b.cri.dimK == 1
def test_09(self):
N = 16
Nd = 5
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N)
try:
b = cbpdn.ConvBPDN(D, s)
b.solve()
except Exception as e:
print(e)
assert 0
def test_12(self):
N = 16
Nd = 5
Cs = 3
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N, Cs)
lmbda = 1e-1
L = 1e3
opt = cbpdn.ConvBPDN.Options({'L': L})
b = cbpdn.ConvBPDN(D, s, lmbda, opt=opt, dimK=0)
b.solve()
assert np.array(b.getitstat().Rsdl)[-1] < 1e-3
def test_18(self):
N = 16
Nd = 5
Cs = 3
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N, Cs)
lmbda = 1e-1
L = 1e3
try:
opt = cbpdn.ConvBPDN.Options({'Monotone': True, 'L': L})
b = cbpdn.ConvBPDN(D, s, lmbda, opt=opt, dimK=0)
b.solve()
except Exception as e:
print(e)
assert 0
assert np.array(b.getitstat().Rsdl)[-1] < 1e-3
def test_06(self):
N = 16
Nd = 5
K = 2
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N, K)
dt = np.float32
opt = cbpdn.ConvBPDN.Options({
'Verbose': False,
'MaxMainIter': 20,
'Backtrack': BacktrackStandard(),
'DataType': dt
})
lmbda = 1e-1
b = cbpdn.ConvBPDN(D, s, lmbda, opt=opt)
b.solve()
assert b.X.dtype == dt
assert b.Xf.dtype == fft.complex_dtype(dt)
assert b.Yf.dtype == fft.complex_dtype(dt)
"""
lmbda = 1e-1
L = 1e1
opt = cbpdn.ConvBPDN.Options({
'Verbose': True,
'MaxMainIter': 250,
'RelStopTol': 8e-5,
'L': L,
'Backtrack': BacktrackStandard(maxiter=15)
})
"""
Initialise and run CSC solver.
"""
b = cbpdn.ConvBPDN(D, sh, lmbda, opt)
X = b.solve()
print("ConvBPDN solve time: %.2fs" % b.timer.elapsed('solve'))
"""
Reconstruct image from sparse representation.
"""
shr = b.reconstruct().squeeze()
imgr = sl + shr
print("Reconstruction PSNR: %.2fdB\n" % sm.psnr(img, imgr))
"""
Display low pass component and sum of absolute values of coefficient maps of highpass component.
"""
fig = plot.figure(figsize=(14, 7))
plot.subplot(1, 2, 1)
"""
lmbda = 5e-2
L = 1.
opt = cbpdn.ConvBPDN.Options({
'Verbose': True,
'MaxMainIter': 250,
'RelStopTol': 1e-4,
'L': L,
'Backtrack': BacktrackStandard(maxiter=15)
})
"""
Initialise and run CSC solver.
"""
b = cbpdn.ConvBPDN(D, sh, lmbda, opt, dimK=0)
X = b.solve()
print("ConvBPDN solve time: %.2fs" % b.timer.elapsed('solve'))
"""
Reconstruct image from sparse representation.
"""
shr = b.reconstruct().squeeze()
imgr = sl + shr
print("Reconstruction PSNR: %.2fdB\n" % psnr(img, imgr))
"""
Display low pass component and sum of absolute values of coefficient maps of highpass component.
"""
fig = plot.figure(figsize=(14, 7))
plot.subplot(1, 2, 1)
