def get_psf(self, subpixel=True, tkhflt=False, tkhlmb=1e-3):
"""Get the estimated psf. If parameter `subpixel` is True, the
subpixel resolution psf is returned, constructed by interpolation
of the psf estimated at the resolution of the input image.
"""
if subpixel:
Hl = lanczos_filters((self.M, self.M), self.K,
collapse_axes=False)
gp = np.pad(self.g, ((0, self.img.shape[0] - self.gshp[0]),
(0, self.img.shape[1] - self.gshp[1])),
'constant')
grsp = fftconv(Hl, gp[..., np.newaxis, np.newaxis],
origin=(self.K, self.K),
axes=(0, 1),
)[0:self.gshp[0], 0:self.gshp[1]]
shp = tuple(np.array(self.gshp) * self.M)
gsub = np.transpose(grsp, (0, 2, 1, 3)).reshape(shp)
gsub[gsub < 0.0] = 0.0
if tkhflt:
gsub, shp = tikhonov_filter(gsub, tkhlmb)
gsub /= np.linalg.norm(gsub)
return gsub
else:
return self.g / np.linalg.norm(self.g)
"""
Load example image.
"""
img = util.ExampleImages().image('kodim23.png', scaled=True, gray=True,
idxexp=np.s_[160:416,60:316])
"""
Highpass filter example image.
"""
npd = 16
fltlmbd = 10
sl, sh = signal.tikhonov_filter(img, fltlmbd, npd)
"""
Load dictionary and display it.
"""
D = util.convdicts()['G:12x12x36']
plot.imview(util.tiledict(D), fgsz=(7, 7))
"""
Set :class:`.admm.cbpdn.ConvBPDNProjL1` solver options.
"""
gamma = 4.05e2
Load a reference image and corrupt it with Gaussian white noise with $\sigma = 0.1$. (The call to ``numpy.random.seed`` ensures that the pseudo-random noise is reproducible.)
"""
img = util.ExampleImages().image('monarch.png',
zoom=0.5,
scaled=True,
idxexp=np.s_[:, 160:672])
np.random.seed(12345)
imgn = img + np.random.normal(0.0, 0.1, img.shape).astype(np.float32)
"""
Highpass filter test image.
"""
npd = 16
fltlmbd = 5.0
imgnl, imgnh = signal.tikhonov_filter(imgn, fltlmbd, npd)
"""
Load dictionary.
"""
D = util.convdicts()['G:8x8x128']
"""
Set solver options. See Section 8 of :cite:`wohlberg-2017-convolutional2` for details of construction of $\ell_1$ weighting matrix $W$.
"""
imgnpl, imgnph = signal.tikhonov_filter(pad(imgn), fltlmbd, npd)
W = fft.irfftn(
np.conj(fft.rfftn(D[..., np.newaxis, :], imgnph.shape[0:2],
(0, 1))) * fft.rfftn(imgnph[..., np.newaxis], None,
(0, 1)), imgnph.shape[0:2], (0, 1))
W = 1.0 / (np.maximum(np.abs(W), 1e-8))
def test_06(self):
img = np.random.randn(16, 16, 16)
iml, imh = signal.tikhonov_filter(img, 2.0, npd=8)
assert iml.shape == img.shape and imh.shape == img.shape
def test_05(self):
img = np.random.randn(64, 64)
iml, imh = signal.tikhonov_filter(img, 5.0)
assert iml.shape == img.shape and imh.shape == img.shape
"""
Load example image.
"""
img = util.ExampleImages().image('kodim23.png', scaled=True,
idxexp=np.s_[160:416,60:316])
"""
Highpass filter example image.
"""
npd = 16
fltlmbd = 10
slc, shc = signal.tikhonov_filter(img, fltlmbd, npd)
"""
Load greyscale convolutional dictionary.
"""
D = util.convdicts()['G:8x8x64']
"""
Learn a standard dictionary $B$ to represent all pixel colours in the example image. Since the standard dictionary is a $3 \times 6$ matrix, the sparse representation $X$ has 6 pseudo-channels, which are converted to the 3 channels of the example image by right-multiplication by the dictionary $B$, giving $XB$.
"""
S = shc.reshape((-1, shc.shape[-1])).T
np.random.seed(12345)
def test_07(self):
img = np.random.randn(64, 64)
iml, imh = signal.tikhonov_filter(img, 5.0)
assert np.isrealobj(iml) and np.isrealobj(imh)
def test_06(self):
img = np.random.randn(64, 64) + 1j * np.random.randn(64, 64)
iml, imh = signal.tikhonov_filter(img, 5.0)
assert np.iscomplexobj(iml) and np.iscomplexobj(imh)
"""
reader = imageio.get_reader('imageio:cockatoo.mp4')
frmlst = []
for i, frm in enumerate(reader):
if i >= 250:
frm = zoom(signal.rgb2gray(frm.astype(np.float32) / 255.0), 0.25)
frmlst.append(frm[20:-20, 70:-70])
vid = np.stack(frmlst, axis=2)
"""
Highpass filter video frames.
"""
npd = 16
fltlmbd = 10
vl, vh = signal.tikhonov_filter(vid, fltlmbd, npd)
"""
Construct initial dictionary.
"""
np.random.seed(12345)
D0 = np.random.randn(5, 5, 3, 25)
"""
Set regularization parameter and options for dictionary learning solver.
"""
lmbda = 0.1
opt = cbpdndl.ConvBPDNDictLearn.Options(
{
'Verbose': True,
'MaxMainIter': 200,
