def test_01(self):
N = 64
L = 20
x = np.cos(np.linspace(0, np.pi, N))[np.newaxis, :]
y = np.cos(np.linspace(0, np.pi, N))[:, np.newaxis]
U = x * y
V = np.random.randn(N, N)
t = np.sort(np.abs(V).ravel())[V.size - L]
V[np.abs(V) < t] = 0
D = U + V
lmbda = 0.1
opt = spline.SplineL1.Options({
'Verbose': False,
'gEvalY': False,
'MaxMainIter': 250,
'RelStopTol': 5e-4,
'DFidWeight': V == 0,
'AutoRho': {
'Enabled': True
}
})
b = spline.SplineL1(D, lmbda, opt)
X = b.solve()
assert (np.abs(b.itstat[-1].ObjFun - 0.333606246) < 1e-6)
assert (sm.mse(U, X) < 1e-6)
def test_02(self):
N = 8
D = np.random.randn(N, N)
lmbda = 0.1
try:
b = spline.SplineL1(D, lmbda)
b.solve()
except Exception as e:
print(e)
assert (0)
def test_03(self):
N = 8
D = np.random.randn(N, N)
lmbda = 0.1
dt = np.float64
opt = spline.SplineL1.Options({
'Verbose': False,
'MaxMainIter': 20,
'AutoRho': {
'Enabled': True
},
'DataType': dt
})
b = spline.SplineL1(D, lmbda, opt=opt)
b.solve()
assert (b.X.dtype == dt)
assert (b.Y.dtype == dt)
assert (b.U.dtype == dt)
imgn = util.spnoise(img, 0.2)
"""
Set regularization parameter and options for ℓ1-spline solver. The regularization parameter used here has been manually selected for good performance.
"""
lmbda = 5.0
opt = spline.SplineL1.Options({'Verbose': True, 'gEvalY': False})
"""
Create solver object and solve, returning the the denoised image ``imgr``.
"""
b = spline.SplineL1(imgn, lmbda, opt)
imgr = b.solve()
"""
Display solve time and denoising performance.
"""
print("SplineL1 solve time: %5.2f s" % b.timer.elapsed('solve'))
print("Noisy image PSNR: %5.2f dB" % metric.psnr(img, imgn))
print("Denoised image PSNR: %5.2f dB" % metric.psnr(img, imgr))
"""
Display reference, corrupted, and denoised images.
"""
