def anchorUpdate_MAP(signal,
kernel,
prior=0,
iterations=10,
measure=True,
clip=True,
verbose=False):
xp = cp.get_array_module(signal)
signal_deconv = signal
signal = signal / signal.sum()
kernel = kernel / kernel.sum()
epsilon = 1e-7
# starting guess with a flat image
if prior.any() == 0:
signal_deconv = xp.full(signal.shape,
0.5) + 0.01 * xp.random.rand(*signal.shape)
else:
signal_deconv = prior #+ 0.1*prior.max()*xp.random.rand(*signal.shape)
# to measure the distance between the guess convolved and the signal
error = None
if measure == True:
error = xp.zeros(iterations)
for i in range(iterations):
if verbose == True:
print('Iteration ' + str(i))
kernel_update = my_convolution(signal_deconv, kernel)
kernel_update = my_correlation(kernel_update, kernel)
kernel_update = kernel_update / kernel_update.sum()
kernel_mirror = axisflip(kernel_update)
relative_blur = my_convolution(signal_deconv, kernel_update)
if measure == True:
error[i] = xp.linalg.norm(signal - relative_blur)
relative_blur = signal / relative_blur
# avoid errors due to division by zero or inf
relative_blur[xp.isinf(relative_blur)] = epsilon
relative_blur = xp.nan_to_num(relative_blur)
# multiplicative update
signal_deconv *= xp.exp(
my_convolution(relative_blur - 1, kernel_mirror))
if clip:
signal_deconv[signal_deconv > +1] = +1
signal_deconv[signal_deconv < -1] = -1
return signal_deconv, error
def maxAPosteriori(signal,
kernel,
iterations=10,
measure=True,
clip=True,
verbose=False):
xp = cp.get_array_module(signal)
signal_deconv = signal
# starting guess with a flat image
signal_deconv = xp.full(signal.shape, 0.5)
epsilon = 1e-7
kernel_mirror = axisflip(kernel)
# to measure the distance between the guess convolved and the signal
error = None
if measure == True:
error = xp.zeros(iterations)
for i in range(iterations):
if verbose == True and (i % 100) == 0:
print('Iteration ' + str(i))
relative_blur = my_convolution(signal_deconv, kernel)
if measure == True:
error[i] = xp.linalg.norm(signal - relative_blur)
relative_blur = signal / relative_blur
# avoid errors due to division by zero or inf
relative_blur[xp.isinf(relative_blur)] = epsilon
relative_blur = xp.nan_to_num(relative_blur)
# multiplicative update
signal_deconv *= xp.exp(
my_convolution(relative_blur - 1, kernel_mirror))
if clip:
signal_deconv[signal_deconv > +1] = +1
signal_deconv[signal_deconv < -1] = -1
return signal_deconv, error
# test[64:-64,64:-64] = satellite[64:-64,64:-64]
# satellite = test.copy()
# normalization
satellite = satellite / satellite.mean()
psf_long /= psf_long.sum()
psf_round /= psf_round.sum()
image1 = satellite.copy()
image2 = satellite.copy()
plt.subplot(121), plt.imshow(image1)
plt.subplot(122), plt.imshow(image2)
# %% test my convolutions
autoconv = pf.my_convolution(image1, image2[::-1, ::-1])
autocorr = pf.my_correlation(image1, image2)
autoconv /= autoconv.max()
autocorr /= autocorr.max()
print(np.array_equal(autoconv, autocorr))
plt.subplot(131), plt.imshow(autoconv)
plt.subplot(132), plt.imshow(autocorr)
plt.subplot(133), plt.imshow(np.abs(autoconv - autocorr))
# test scipy convolutions
autoconv = signal.convolve(image1,
image2[::-1, ::-1],
mode='same',
method='fft')
def anchorUpdateSK(signal, kernel, signal_deconv=np.float32(0), iterations=10, measure=True, clip=False, verbose=True):
# for code agnosticity between Numpy/Cupy
xp = cp.get_array_module(signal)
xps = cupyx.scipy.get_array_module(signal)
# for performance evaluation
start_time = time.time()
if iterations<100:
breakcheck = iterations
else:
breakcheck = 100
# normalization
signal /= signal.sum()
epsilon = 1e-7
# starting guess with a flat image
if signal_deconv.any()==0:
# xp.random.seed(0)
signal_deconv = xp.full(signal.shape,0.5) + 0.01*xp.random.rand(*signal.shape)
# signal_deconv = signal.copy()
else:
signal_deconv = signal_deconv #+ 0.1*prior.max()*xp.random.rand(*signal.shape)
# normalization
signal_deconv = signal_deconv/signal_deconv.sum()
# to measure the distance between the guess convolved and the signal
error = None
if measure == True:
error = xp.zeros(iterations)
for i in range(iterations):
# I use this property to make computation faster
kernel_update = xps.ndimage.gaussian_filter(signal_deconv, sigma)
# kernel_update = xps.ndimage.fourier_gaussian(signal_deconv, sigma)
kernel_mirror = (kernel_update)
relative_blur = my_correlation(signal_deconv, kernel_update)
# compute the measured distance metric if given
if measure==True:
# error[i] = xp.linalg.norm(signal/signal.sum()-relative_blur/relative_blur.sum())
error[i] = snrIntensity_db(signal/signal.sum(), xp.abs(signal/signal.sum()-relative_blur/relative_blur.sum()))
if (error[i] < error[i-breakcheck]) and i > breakcheck:
break
if verbose==True and (i % 100)==0 and measure==False:
print('Iteration ' + str(i))
elif verbose==True and (i % 100)==0 and measure==True:
print('Iteration ' + str(i) + ' - noise level: ' + str(error[i]))
relative_blur = signal / relative_blur
# avoid errors due to division by zero or inf
relative_blur[xp.isinf(relative_blur)] = epsilon
relative_blur = xp.nan_to_num(relative_blur)
# multiplicative update, for the full model
signal_deconv *= 0.5 * (my_convolution(relative_blur, kernel_mirror) + my_correlation(axisflip(relative_blur), kernel_mirror))
# signal_deconv *= (my_convolution(relative_blur, kernel_mirror) + my_correlation(relative_blur,kernel_mirror))
# multiplicative update, for the Anchor Update approximation
# signal_deconv *= my_convolution(kernel_mirror, relative_blur)
# multiplicative update, remaining term. This gives wrong reconstructions
# signal_deconv *= my_correlation(axisflip(relative_blur), kernel_mirror)
if clip:
signal_deconv[signal_deconv > +1] = +1
signal_deconv[signal_deconv < -1] = -1
print("\n\n Algorithm finished. Performance:")
print("--- %s seconds ----" % (time.time() - start_time))
print("--- %s sec/step ---" % ((time.time() - start_time)/iterations))
return signal_deconv, error #,kernel_update
satellite = satellite / satellite.mean()
# psf normalization
psf_long /= psf_long.sum()
psf_round /= psf_round.sum()
# noise parameters and number of iterations
lambd = 2**4
iterations = 10000
# %% creating the measurement described in the experiment A - if results do not converse, re-run several times until snr grows
noise = (np.random.poisson(lam=lambd, size=satellite.shape))
measureA = pf.my_autocorrelation(satellite)
measureA = (2**16) * measureA / measureA.max()
measureA_blur = pf.my_convolution(measureA, psf_long)
measureA_blur_noise = np.abs(measureA_blur + noise - lambd)
# running the algorithm
deconvolved_A, error_A = pd.anchorUpdateX(cp.asarray(measureA_blur_noise),
cp.asarray(psf_long),
cp.asarray(0),
kerneltype='A',
iterations=iterations)
deconvolved_A, error_A = deconvolved_A.get(), error_A.get()
deconvolved_A = pf.my_alignND(satellite, (deconvolved_A))
deconvolved_A = deconvolved_A / deconvolved_A.mean()
plt.figure(1)
plt.subplot(221), plt.imshow(satellite,
def anchorUpdateZ(signal,
kernel,
signal_deconv=np.float32(0),
kerneltype='B',
iterations=10,
measure=True,
clip=False,
verbose=True):
"""
Reconstruction of signal_deconv from its auto-correlation signal, via a
RichardsonLucy-like multiplicative procedure. At the same time, the kernel
psf is deconvolved from the reconstruction so that the iteration converges
corr(conv(signal_deconv, kernel), conv(signal_deconv, kernel),) -> signal.
Parameters
----------
signal : ndarray, either numpy or cupy.
The auto-correlation to be inverted
kernel : ndarray, either numpy or cupy.
Point spread function that blurred the signal. It must be
signal.shape == kernel.shape.
signal_deconv : ndarray, either numpy or cupy or 0. It must be signal.shape == signal_deconv.shape.
The de-autocorrelated signal deconvolved with kernel at ith iteration. The default is np.float32(0).
kerneltype : string.
Type of kernel update used for the computation choosing from blurring
directly the autocorrelation 'A', blurring the signal that is then
autocorrelated 'B' and the window applied in fourier domain 'C'.
The default is 'B'.
iterations : int, optional
Number of iteration to be done. The default is 10.
measure : boolean, optional
If true computes the euclidean distance between signal and the
auto-correlation of signal_deconv. The default is True.
clip : boolean, optional
Clip the results within the range -1 to 1. Useless for the moment. The default is False.
verbose : boolean, optional
Print current step value. The default is True.
Returns
-------
signal_deconv : ndarray, either numpy or cupy.
The de-autocorrelated signal deconvolved with kernel at ith iteration..
error : vector.
Euclidean distance between signal and the auto-correlation of signal_deconv.
Last implementation returns the SNR instead of euclidean distance.
"""
# for code agnosticity between Numpy/Cupy
xp = pyb.get_array_module(signal)
# for performance evaluation
start_time = time.time()
if iterations < 100:
breakcheck = iterations
else:
breakcheck = 100
# normalization
signal /= signal.sum()
kernel /= kernel.sum()
epsilon = 1e-7
# starting guess with a flat image
if signal_deconv.any() == 0:
# xp.random.seed(0)
signal_deconv = xp.full(signal.shape,
0.5) + 0.01 * xp.random.rand(*signal.shape)
# signal_deconv = signal.copy()
else:
signal_deconv = signal_deconv #+ 0.1*prior.max()*xp.random.rand(*signal.shape)
# normalization
signal_deconv = signal_deconv / signal_deconv.sum()
# to measure the distance between the guess convolved and the signal
error = None
if measure == True:
error = xp.zeros(iterations)
for i in range(iterations):
# I use this property to make computation faster
K = my_convolution(signal_deconv, my_correlation(kernel, kernel))
relative_blur = my_correlation(K, signal_deconv)
# compute the measured distance metric if given
if measure == True:
#error[i] = xp.linalg.norm(signal/signal.sum()-relative_blur/relative_blur.sum())
error[i] = snrIntensity_db(
signal / signal.sum(),
xp.abs(signal / signal.sum() -
relative_blur / relative_blur.sum()))
if (error[i] < error[i - breakcheck]) and i > breakcheck:
break
if verbose == True and (i % 100) == 0 and measure == False:
print('Iteration ' + str(i))
elif verbose == True and (i % 100) == 0 and measure == True:
print('Iteration ' + str(i) + ' - noise level: ' + str(error[i]))
relative_blur = signal / relative_blur
# avoid errors due to division by zero or inf
relative_blur[xp.isinf(relative_blur)] = epsilon
relative_blur = xp.nan_to_num(relative_blur)
# multiplicative update, for the full model
# signal_deconv *= 0.5 * (my_convolution(relative_blur, kernel_mirror) + my_correlation(axisflip(relative_blur), kernel_mirror))
# signal_deconv *= (my_convolution(kernel_mirror,relative_blur) + my_correlation(relative_blur, kernel_mirror))
# multiplicative update, for the Anchor Update approximation
signal_deconv *= my_correlation((relative_blur), (K))
# signal_deconv *= (my_correlation(relative_blur, K) + my_convolution(relative_blur, K))
# multiplicative update, remaining term. This gives wrong reconstructions
# signal_deconv *= my_correlation(axisflip(relative_blur), kernel_mirror)
if clip:
signal_deconv[signal_deconv > +1] = +1
signal_deconv[signal_deconv < -1] = -1
print("\n\n Algorithm finished. Performance:")
print("--- %s seconds ----" % (time.time() - start_time))
print("--- %s sec/step ---" % ((time.time() - start_time) / iterations))
return signal_deconv, error #,kernel_update
def invert_autoconvolution(magnitude,
prior=None,
mask=None,
measure=True,
steps=200,
mode='deautocorrelation',
verbose=True):
# agnostic code, xp is either numpy or cupy depending on the magnitude array module
xp = pyb.get_array_module(magnitude)
# object support constraint
if mask is None:
mask = xp.ones(magnitude.shape)
# assert magnitude.shape == mask.shape, 'mask and magnitude should have same shape'
assert steps > 0, 'steps should be a positive number'
assert mode == 'deautoconvolution' or mode == 'deautocorrelation',\
'mode should be \'deautoconvolution\' or \'deautocorrelation\''
# random phase if prior is None, otherwise start with the prior Fourier
if prior is None:
x_hat = 1 + 0.01 * xp.random.rand(*magnitude.shape)
else:
x_hat = prior
if measure == True:
ratio = xp.zeros(steps)
else:
ratio = None
x_hat = x_hat * mask
y_mes = 0.5 * (magnitude + magnitude[::-1, ::-1])
# normalization for energy preservation
y0 = (xp.sum(x_hat))**2
# y0 = (xp.sum(x_hat))
x_hat = xp.divide(x_hat, xp.sqrt(y0))
# monitoring the convergence of the solution
# convergence = xp.zeros(steps)
# loop for the minimization, I guess there can be an analogue for the autocorrelation
if mode == "deautoconvolution":
for i in range(0, steps):
y = my_convolution(x_hat, x_hat)
# u_hat = y_mes / y
# zero divided by zero is equal to zero
u_hat = xp.divide(y_mes, y, out=xp.zeros_like(y_mes), where=y != 0)
if measure == True:
ratio[i] = u_hat.mean()
# convergence[i] = xp.mean(u_hat)
r_hat = 1 / xp.sqrt(y0) * my_convolution(u_hat, x_hat)
x_hat = x_hat * r_hat
# not ready yet
elif mode == "deautocorrelation":
for i in range(0, steps):
y = my_correlation(x_hat, x_hat)
# if measure==True:
# ratio[i] = xp.linalg.norm(y_mes - y)
u_hat = xp.divide(y_mes, y, out=xp.zeros_like(y_mes), where=y != 0)
if measure == True:
ratio[i] = u_hat.mean()
r_hat = (0.5 / xp.sqrt(y0)) * (my_correlation(x_hat, u_hat) +
(my_convolution(x_hat, u_hat)))
# r_hat = (0.5/(y0)) * ( my_correlation(x_hat[::-1,::-1], u_hat) + my_convolution(x_hat, u_hat) )
x_hat = x_hat * r_hat
# r_hat = (1/xp.sqrt(y0)) * my_correlation(x_hat[::-1,::-1], u_hat)
# x_hat = x_hat * r_hat
return (x_hat, ratio)
def schulzSnyder(correlation,
prior=np.float32(0),
iterations=10,
measure=True,
clip=False,
verbose=True):
"""
De-AutoCorrelation protocol implemented by Schultz-Snyder. It needs to be
checked to assess the working procedure.
Parameters
----------
correlation : TYPE
DESCRIPTION.
prior : TYPE, optional
DESCRIPTION. The default is np.float32(0).
iterations : TYPE, optional
DESCRIPTION. The default is 10.
measure : TYPE, optional
DESCRIPTION. The default is True.
clip : TYPE, optional
DESCRIPTION. The default is True.
verbose : TYPE, optional
DESCRIPTION. The default is True.
Returns
-------
signal_decorr : TYPE
DESCRIPTION.
error : TYPE
DESCRIPTION.
"""
xp = pyb.get_array_module(correlation)
# for performance evaluation
start_time = time.time()
epsilon = 1e-7
if iterations < 10:
breakcheck = iterations
else:
breakcheck = 10
# starting guess with a flat image
if prior.any() == 0:
signal_decorr = xp.full(
correlation.shape, 0.5) + 0.01 * xp.random.rand(*correlation.shape)
else:
signal_decorr = prior.copy(
) #+ 0.1*prior.max()*xp.random.rand(*signal.shape)
R_0 = signal_decorr.sum()
signal_decorr = signal_decorr / R_0
relative_corr = xp.zeros_like(signal_decorr)
# to measure the distance between the guess convolved and the signal
error = None
if measure == True:
error = xp.zeros(iterations)
for i in range(iterations):
relative_corr = my_correlation(signal_decorr, signal_decorr)
if measure == True:
# error[i] = xp.linalg.norm(correlation/correlation.sum()-relative_corr/relative_corr.sum())
error[i] = snrIntensity_db(
correlation / correlation.sum(),
xp.abs(correlation / correlation.sum() -
relative_corr / relative_corr.sum()))
if (error[i] < error[i - breakcheck]) and i > breakcheck:
break
if verbose == True and (i % 100) == 0 and measure == False:
print('Iteration ' + str(i))
elif verbose == True and (i % 100) == 0 and measure == True:
print('Iteration ' + str(i) + ' - noise level: ' + str(error[i]))
# relative_corr = 0.5*(correlation + axisflip(correlation)) / relative_corr
relative_corr = (correlation) / relative_corr
# avoid errors due to division by zero or inf
relative_corr[xp.isinf(relative_corr)] = epsilon
relative_corr = xp.nan_to_num(relative_corr)
# multiplicative update
# signal_decorr *= my_correlation(axisflip(signal_decorr), (relative_corr)) / R_0
# signal_decorr *= my_correlation((relative_corr), (signal_decorr)) / R_0
# signal_decorr *= (my_correlation(relative_corr, signal_decorr) + my_correlation(relative_corr, axisflip(signal_decorr))) / R_0
signal_decorr *= (my_correlation(relative_corr, signal_decorr) +
my_convolution(relative_corr, signal_decorr)) / R_0
if clip:
signal_decorr[signal_decorr > +1] = +1
signal_decorr[signal_decorr < -1] = -1
print("\n\n Algorithm finished. Performance:")
print("--- %s seconds ----" % (time.time() - start_time))
print("--- %s sec/step ---" % ((time.time() - start_time) / iterations))
return signal_decorr, error
def maxAPosteriori(signal,
kernel,
iterations=10,
measure=True,
clip=True,
verbose=False):
"""
Deconvolution using the Maximum a Posteriori algorithm. Implementation
identical to Richardson Lucy algorithm but with a different moltiplicative
rule for the update.
Parameters
----------
signal : ndarray, either numpy or cupy.
The signal to be deblurred.
kernel : ndarray, either numpy or cupy.
Point spread function that blurred the signal. It must be
signal.shape == kernel.shape.
prior : ndarray, either numpy or cupy, optional
the prior information to start the reconstruction. The default is np.float32(0).
iterations : integer, optional
Number of iteration to be done. The default is 10.
measure : boolean, optional
If true computes the euclidean distance between signal and the auto-correlation of signal_deconv. The default is True.
clip : boolean, optional
Clip the results within the range -1 to 1. The default is False.
verbose : boolean, optional
Print current step value. The default is True.
Returns
-------
signal_deconv : ndarray, either numpy or cupy.
The deconvolved signal with respect the given kernel at ith iteration.
error : one dimensional ndarray.
Euclidean distance between signal and the auto-correlation of signal_deconv.
"""
xp = pyb.get_array_module(signal)
start_time = time.time()
epsilon = 1e-7
# starting guess with a flat image
if prior.any() == 0:
signal_deconv = xp.full(signal.shape,
0.5) + 0.01 * xp.random.rand(*signal.shape)
else:
signal_deconv = prior #+ 0.1*prior.max()*xp.random.rand(*signal.shape)
kernel_mirror = axisflip(kernel)
error = None
if measure == True:
error = xp.zeros(iterations)
for i in range(iterations):
if verbose == True and (i % 100) == 0:
print('Iteration ' + str(i))
relative_blur = my_convolution(signal_deconv, kernel)
if measure == True:
error[i] = xp.linalg.norm(signal / signal.sum() -
relative_blur / relative_blur.sum())
relative_blur = signal / relative_blur
# avoid errors due to division by zero or inf
relative_blur[xp.isinf(relative_blur)] = epsilon
relative_blur = xp.nan_to_num(relative_blur)
# multiplicative update given by the MAP
signal_deconv *= xp.exp(
my_convolution(relative_blur - 1, kernel_mirror))
if clip:
signal_deconv[signal_deconv > +1] = +1
signal_deconv[signal_deconv < -1] = -1
print("\n\n Algorithm finished. Performance:")
print("--- %s seconds ----" % (time.time() - start_time))
print("--- %s sec/step ---" % ((time.time() - start_time) / iterations))
return signal_deconv, error
def anchorUpdate_H(signal,
kernel,
signal_deconv=np.float32(0),
iterations=10,
measure=True,
clip=False,
verbose=True):
xp = cp.get_array_module(signal)
start_time = time.time()
signal = signal / signal.sum()
# kernel = kernel / kernel.sum()
# compute the norm of the fourier transform of the kernel
# kernel = xp.fft.rfftn(kernel)
epsilon = 1e-7
# starting guess with a flat image
if signal_deconv.any() == 0:
signal_deconv = xp.full(signal.shape,
0.5) + 0.01 * xp.random.rand(*signal.shape)
else:
signal_deconv = signal_deconv #+ 0.1*prior.max()*xp.random.rand(*signal.shape)
signal_deconv = signal_deconv / signal_deconv.sum()
# to measure the distance between the guess convolved and the signal
error = None
if measure == True:
error = xp.zeros(iterations)
for i in range(iterations):
if verbose == True and (i % 100) == 0:
print('Iteration ' + str(i))
# I use this property to make computation faster
# kernel_update = my_correlation_withfft(signal_deconv, kernel)
kernel_update = my_correlation(signal_deconv, kernel)
# kernel_update = kernel_update / kernel_update.sum()
kernel_mirror = axisflip(kernel_update)
relative_blur = my_convolution(signal_deconv, kernel_update)
if measure == True:
error[i] = xp.linalg.norm(signal / signal.max() -
relative_blur / relative_blur.max())
relative_blur = signal / relative_blur
# avoid errors due to division by zero or inf
relative_blur[xp.isinf(relative_blur)] = epsilon
relative_blur = xp.nan_to_num(relative_blur)
# multiplicative update
signal_deconv *= my_convolution(relative_blur, kernel_mirror)
if clip:
signal_deconv[signal_deconv > +1] = +1
signal_deconv[signal_deconv < -1] = -1
print("\n\n Algorithm finished. Performance:")
print("--- %s seconds ----" % (time.time() - start_time))
print("--- %s sec/step ---" % ((time.time() - start_time) / iterations))
return signal_deconv, error
def anchorUpdate(signal,
kernel,
signal_deconv=np.float32(0),
iterations=10,
measure=True,
clip=False,
verbose=True):
"""
Reconstruction of signal_deconv from its auto-correlation signal, via a
RichardsonLucy-like multiplicative procedure. At the same time, the kernel
psf is deconvolved from the reconstruction so that the iteration converges
corr(conv(signal_deconv, kernel), conv(signal_deconv, kernel),) -> signal.
Parameters
----------
signal : ndarray, either numpy or cupy.
The auto-correlation to be inverted
kernel : ndarray, either numpy or cupy. It must be signal.shape == kernel.shape.
DESCRIPTION.
signal_deconv : ndarray, either numpy or cupy or 0. It must be signal.shape == signal_deconv.shape.
The de-autocorrelated signal deconvolved with kernel at ith iteration. The default is np.float32(0).
iterations : int, optional
Number of iteration to be done. The default is 10.
measure : boolean, optional
If true computes the euclidean distance between signal and the auto-correlation of signal_deconv. The default is True.
clip : boolean, optional
Clip the results within the range -1 to 1. Useless for the moment. The default is False.
verbose : boolean, optional
Print current step value. The default is True.
Returns
-------
signal_deconv : ndarray, either numpy or cupy.
The de-autocorrelated signal deconvolved with kernel at ith iteration..
error : vector.
Euclidean distance between signal and the auto-correlation of signal_deconv.
"""
xp = cp.get_array_module(signal)
start_time = time.time()
signal = signal / signal.sum()
kernel = kernel / kernel.sum()
epsilon = 1e-7
# starting guess with a flat image
if signal_deconv.any() == 0:
signal_deconv = xp.full(signal.shape,
0.5) + 0.01 * xp.random.rand(*signal.shape)
else:
signal_deconv = signal_deconv
# to measure the distance between the guess convolved and the signal
error = None
if measure == True:
error = xp.zeros(iterations)
for i in range(iterations):
if verbose == True and (i % 100) == 0:
print('Iteration ' + str(i))
# kernel update rule
kernel_update = my_convolution(signal_deconv, kernel)
kernel_update = my_correlation(kernel_update, kernel)
kernel_update = kernel_update / kernel_update.sum()
kernel_mirror = axisflip(kernel_update)
relative_blur = my_convolution(signal_deconv, kernel_update)
if measure == True:
error[i] = xp.linalg.norm(signal - relative_blur)
relative_blur = signal / relative_blur
# avoid errors due to division by zero or inf
relative_blur[xp.isinf(relative_blur)] = epsilon
relative_blur = xp.nan_to_num(relative_blur)
# multiplicative update
signal_deconv *= my_convolution((relative_blur), kernel_mirror)
if clip:
signal_deconv[signal_deconv > +1] = +1
signal_deconv[signal_deconv < -1] = -1
print("\n\n Algorithm finished. Performance:")
print("--- %s seconds ----" % (time.time() - start_time))
print("--- %s sec/step ---" % ((time.time() - start_time) / iterations))
return signal_deconv, error
