def get_best_wiener(y, h, Nws=50):
ws = 1.0e-6 * 2 ** np.arange(Nws)
return ws[np.argmin([mse(x, deconv_wiener(y, h, w)) for w in ws])]
return ws[np.argmin([mse(x, deconv_wiener(y, h, w)) for w in ws])]
if __name__ == "__main__":
np.random.seed(0)
# 2d
from matplotlib.pyplot import imread
s = 0.05
x = imread("data/usaf.png")
x *= 0
x[200:400, 200:400] = 1.0
mu = 10000.0
rho = 10.0
scale = 100
x *= 1.0 * scale
h = psf(x.shape, (6.0, 6.0))
y = myconvolve(x, h) + s * np.amax(x) * np.random.uniform(0, 1, x.shape)
# u = deconv_tv_al([y,y2],[h,h2])
u = deconv_tv_al(y, h, mu, rho)
u2 = deconv_wiener(y, h, rho / mu)
h = psf(x.shape,(5.,5.))
h2 = psf(x.shape,(11,3))
y = myconvolve(x,h)+s*np.amax(x)*np.random.uniform(0,1,x.shape)
y2 = myconvolve(x,h2)+s*np.amax(x)*np.random.uniform(0,1,x.shape)
# u = deconv_tv_al([y,y2],[h,h2])
u = deconv_tv_al(y,h,10.,1.)
u2 = deconv_wiener(y,h,0.1)
#3d
from spimagine import read3dTiff
x = read3dTiff("data/usaf3d.tif")[100:228,100:228,100:228]
h = psf(x.shape,(3.,3.,11))
h2 = psf(x.shape,(11,3,3))
h3 = psf(x.shape,(3,11,3))
y = myconvolve(x,h)+.1*np.amax(x)*np.random.uniform(0,1,x.shape)
y2 = myconvolve(x,h2)+.1*np.amax(x)*np.random.uniform(0,1,x.shape)
y3 = myconvolve(x,h3)+.1*np.amax(x)*np.random.uniform(0,1,x.shape)
u = deconv_tv_al([y,y2,y3],[h,h2,h3],100.,1.)
def err(gamma):
u = deconv_wiener(y, h, gamma=gamma)
u *= np.mean(y) / np.mean(u)
return rms(d0, u)
def gmin_for_sig(sig_level=0.1, maxiter=20):
d0, y, noise, h = form_image(sig_level)
g = get_gamma_min(d0, y, h, maxiter=maxiter)
u = deconv_wiener(y, h, g)
return d0, y, h, u, g
np.random.seed(0)
# 2d
from matplotlib.pyplot import imread
x = imread("data/usaf.png")
x = np.pad(x, ((256,) * 2,) * 2, mode="constant")
h = psf(x.shape, (3.0, 11))
h2 = psf(x.shape, (11, 3))
y = myconvolve(x, h) + 0.3 * np.amax(x) * np.random.uniform(0, 1, x.shape)
y2 = myconvolve(x, h2) + 0.3 * np.amax(x) * np.random.uniform(0, 1, x.shape)
u1 = deconv_wiener([y, y2], [h, h2], 1.0e-6)
u2 = deconv_wiener2([y, y2], [h2, h], 1.0e-6)
# #3d
# from spimagine import read3dTiff
#
# x = read3dTiff("data/usaf3d.tif")[100:228,100:228,100:228]
# h = psf(x.shape,(3.,3.,11))
#
# h2 = psf(x.shape,(11,3,3))
# h3 = psf(x.shape,(3,11,3))
#
# y = myconvolve(x,h)+.1*np.amax(x)*np.random.uniform(0,1,x.shape)
# y2 = myconvolve(x,h2)+.1*np.amax(x)*np.random.uniform(0,1,x.shape)
# y3 = myconvolve(x,h3)+.1*np.amax(x)*np.random.uniform(0,1,x.shape)
