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)
def _fun(u , mu):
gu = np.array(np.gradient(u))
bu = myconvolve(u ,h)
l1_part = np.mean(reduce(np.add,[np.abs(_g) for _g in np.array(np.gradient(u))]))
l2_part = np.mean((bu-y)**2)
return l1_part + .5*mu*l2_part
#u = deconv_rl([g1,g2,g3],[h1,h2,h3],20, gamma = 0.000001)
# a2 = deconv_rl([g1,g2],[h1,h2],10, gamma = 0.000001)
# a3 = deconv_rl([g1,g2,g3],[h1,h2,h3],10, gamma = 0.000001)
# a4 = deconv_rl([g1,g2,g3,g4],[h1,h2,h3,h4],10, gamma = 0.000001)
#
# u1 = deconv_rl([g1],[h1],20, gamma = 0.000001)
# u2 = deconv_rl([g2],[h2],20, gamma = 0.000001)
# u3 = deconv_rl([g3],[h3],20, gamma = 0.000001)
# u4 = deconv_rl([g4],[h4],10, gamma = 0.000001)
hs = [np.roll(psf_airy(im.shape, _d),2*i,0) for i,_d in enumerate([(10,20),(20,10),(15,15)])]
gs = [myconvolve(im ,h)+.1*np.amax(im)*np.random.normal(0,1.,im.shape) for h in hs]
u = deconv_rl([gs[0]]*3,[hs[0]]*3,20, gamma = 0.000001, fft_is_unitary=True)
# from itertools import product
# import pylab
# N = len(gs)
# u = []
# for i,(g,h) in enumerate(zip(product(gs,repeat=2),product(hs,repeat=2))):
# u.append(deconv_rl(g,h,5,gamma = 1.e-5))
# pylab.subplot(N,N,i+1)
# pylab.imshow(u[-1])
return f
if __name__ == '__main__':
from matplotlib.pyplot import imread
from pydeconv.utils import myconvolve, psf
im = imread("../tests2/data/usaf.png")
np.random.seed(0)
hx = (5.,5.)
h = psf(im.shape,hx)
g = myconvolve(im ,h)
g += .01*np.amax(im)*np.random.normal(0,1.,im.shape)
mu = 10000.
rho = 2.
def wien(y_f,alpha = .1):
u_f = h_f.conjugate()*y_f
u_f /= np.abs(h_f)**2 + alpha
return np.abs(np.fft.ifftn(u_f))
def _fun(u , mu):
gu = np.array(np.gradient(u))
bu = myconvolve(u ,h)
