def test_06(self):
x = np.random.randn(9, 8)
y = interp.bilinear_demosaic(x)
assert np.array_equal(x[1::2, 1::2], y[1::2, 1::2, 0])
assert np.array_equal(x[0::2, 1::2], y[0::2, 1::2, 1])
assert np.array_equal(x[1::2, 0::2], y[1::2, 0::2, 1])
assert np.array_equal(x[0::2, 0::2], y[0::2, 0::2, 2])
def demosaic(cfaimg):
return bilinear_demosaic(cfaimg)
"""
Define proximal operator of (implicit, unknown) regularisation term for PPP problem. In this case we use BM3D :cite:`dabov-2008-image` as the denoiser, using the [code](https://pypi.org/project/bm3d) released with :cite:`makinen-2019-exact`.
"""
bsigma = 6.1e-2 # Denoiser parameter
def proxg(x, rho):
return bm3d_rgb(x, bsigma)
"""
Construct a baseline solution and initaliser for the PPP solution by BM3D denoising of a simple bilinear demosaicing solution. The `3 * nsigma` denoising parameter for BM3D is chosen empirically for best performance.
"""
imgb = bm3d_rgb(bilinear_demosaic(sn), 3 * nsigma)
"""
Set algorithm options for PPP solver, including use of bilinear demosaiced solution as an initial solution.
"""
opt = PPP.Options({
'Verbose': True,
'RelStopTol': 1e-3,
'MaxMainIter': 12,
'rho': 1.8e-1,
'Y0': imgb
})
"""
Create solver object and solve, returning the the demosaiced image ``imgp``.
"""
