def test_08(self):
lmbda = 3
opt = tvl1.TVL1Denoise.Options({'MaxMainIter': 20})
b = tvl1.TVL1Denoise(self.D, lmbda, opt)
b.solve()
opt['Y0'] = b.Y
try:
c = tvl1.TVL1Denoise(self.D, lmbda, opt)
c.solve()
except Exception as e:
print(e)
assert 0
def test_01(self):
lmbda = 3
try:
b = tvl1.TVL1Denoise(self.D, lmbda)
b.solve()
except Exception as e:
print(e)
assert 0
def test_01(self):
lmbda = 3
opt = tvl1.TVL1Denoise.Options({
'Verbose': False,
'gEvalY': False,
'MaxMainIter': 250
})
b = tvl1.TVL1Denoise(self.D, lmbda, opt, axes=(0, 1, 2))
X = b.solve()
assert cp.abs(b.itstat[-1].ObjFun - 6219.6209699337605) < 1e-6
assert sm.mse(self.U, X) < 1e-6
def test_01(self):
lmbda = 3
opt = tvl1.TVL1Denoise.Options({
'Verbose': False,
'gEvalY': False,
'MaxMainIter': 250
})
b = tvl1.TVL1Denoise(self.D, lmbda, opt)
X = b.solve()
assert cp.abs(b.itstat[-1].ObjFun - 447.78101756451662) < 1e-6
assert sm.mse(self.U, X) < 1e-6
def test_05(self):
lmbda = 3
dt = cp.float64
opt = tvl1.TVL1Denoise.Options({
'Verbose': False,
'MaxMainIter': 20,
'AutoRho': {
'Enabled': True
},
'DataType': dt
})
b = tvl1.TVL1Denoise(self.D, lmbda, opt=opt)
b.solve()
assert b.X.dtype == dt
assert b.Y.dtype == dt
assert b.U.dtype == dt
"""
Create solver object and solve, returning the the denoised image ``imgr``.
"""
if not cupy_enabled():
print('CuPy/GPU device not available: running without GPU acceleration\n')
else:
id = select_device_by_load()
info = gpu_info()
if info:
print('Running on GPU %d (%s)\n' % (id, info[id].name))
b = tvl1.TVL1Denoise(np2cp(imgn), lmbda, opt)
imgr = cp2np(b.solve())
"""
Display solve time and denoising performance.
"""
print("TVL1Denoise 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.
"""
