def body(i, decon):
# Richardson-Lucy Iteration - logic taken largely from a combination of
# the scikit-image (real domain) and DeconvolutionLab2 implementations (complex domain)
conv1 = conv(decon, kern_fft)
# High-pass filter to avoid division by very small numbers (see DeconvolutionLab2)
blur1 = tf.where(conv1 < self.epsilon, tf.zeros_like(datat), datat / conv1, name='blur1')
conv2 = conv(blur1, kern_fft_conj)
# Positivity constraint on result for iteration
decon = tf.maximum(decon * conv2, 0.)
# If given an "observer", pass the current image restoration and iteration counter to it
if self.observer_fn is not None:
# Remove any cropping that may have been added as this is usually not desirable in observers
decon_crop = unpad_around_center(decon, tf.shape(datah))
_, i, decon = tf_observer([decon_crop, i, decon], self.observer_fn)
return i + 1, decon
def tf_fn():
xt = tf.constant(x)
yt = tf_ops.pad_around_center(xt, **kwargs)
zt = tf_ops.unpad_around_center(yt, tf.shape(xt))
return zt
def body(
i,
decon,
):
'''# Richardson-Lucy Iteration - logic taken largely from a combination of
# the scikit-image (real domain) and DeconvolutionLab2 implementations (complex domain)
# conv1 is the current model blurred with the PSF
conv1 = conv(decon, kern_fft)
# High-pass filter to avoid division by very small numbers (see DeconvolutionLab2)
blur1 = tf.where(conv1 < self.epsilon, tf.zeros_like(datat), datat / conv1, name='blur1')
# conv2 is the blurred model convolved with the flipped PSF
conv2 = conv(blur1, kern_fft_conj)
# Positivity constraint on result for iteration
decon = tf.maximum(decon * conv2, 0.)
'''
# Gold algorithm, ratio method, simpler then RL, doesnt use flipped OTF
# conv1 is the current model blurred with the PSF
conv1 = conv(decon, kern_fft)
# High-pass filter to avoid division by very small numbers (see DeconvolutionLab2)?
# we wont do it here as we will use the delta parameter in denom and numerrator of division to get blur2
# as per Stephan Ludwig et al 2019
# should normalise blur2 and decon each time because numbers get big and we risk overflow when multiplying in next step
conv1norm = conv1 / (tf.math.reduce_sum(conv1))
datatNorm = datat / (tf.math.reduce_sum(datat))
# this value seems to work well fo rthe images that are normalised to sum of 1
deltaParam = 1e-4
ratio = (datatNorm + deltaParam) / (conv1norm + deltaParam)
#blur1 = tf.where(conv1 < self.epsilon, tf.zeros_like(datat), datat / conv1, name='blur1')
#ratioNorm = ratio / (tf.math.reduce_sum(ratio))
#deconNorm = decon / (tf.math.reduce_sum(decon))
# decon is the normalised blurred model multiplied by the model
# Positivity constraint on result for iteration
decon = tf.maximum(decon * ratio, 0.)
# Smooth the intermediate result image with Gaussian of sigma 1 every 5th iteration
# to control noise buildup that Gold method is succeptible to.
# Use tf.nn.conv3d to convolve a Gaussian kernel with an image:
# Make Gaussian Kernel with desired specs using gaussian_kernel function defined above
if i % 5 == 0:
# Convolve decon with gauss kernel.
tf.nn.conv3d(decon,
filter=gaussKernel,
strides=[1, 1, 1, 1, 1],
padding="SAME")
# normalise the result so the sum of the data is 1
decon = decon / (tf.math.reduce_sum(decon))
# TODO - Smoothing every 5 iterations with gaussian or wiener.
# TODO rescale back to input data sum intensity - probably need to adjust deltaParam too.
# If given an "observer", pass the current image restoration and iteration counter to it
if self.observer_fn is not None:
# Remove any cropping that may have been added as this is usually not desirable in observers
decon_crop = unpad_around_center(decon, tf.shape(datah))
# normalise the result so the sum of the data is 1
decon_crop = decon_crop / (tf.math.reduce_sum(decon_crop))
# we can use these captured observed tensors to evaluate eg convergence
# in eg. the observer function used.
_, i, decon, conv1 = tf_observer([decon_crop, i, decon, conv1],
self.observer_fn)
return i + 1, decon
def _build_tf_graph(self):
niter = self._get_niter()
# Create argument placeholders with same defaults as those used at graph construction time
padmodh = tf.compat.v1.placeholder_with_default(DEFAULT_PAD_MODE, (), name='pad_mode')
smodeh = tf.compat.v1.placeholder_with_default(DEFAULT_START_MODE, (), name='start_mode')
padminh = tf.compat.v1.placeholder_with_default(tf.zeros(self.n_dims, dtype=tf.int32), self.n_dims, name='pad_min')
# Data and kernel should have shapes (z, height, width)
dataph = tf.compat.v1.placeholder(self.dtype, shape=[None] * self.n_dims, name='data')
kernph = tf.compat.v1.placeholder(self.dtype, shape=[None] * self.n_dims, name='kernel')
datah, kernh = self._wrap_input(dataph), self._wrap_input(kernph)
# Add assertion operations to validate padding mode, start mode, and data/kernel dimensions
flag_pad_mode = tf.stack([tf.equal(padmodh, OPM_LOG2), tf.equal(padmodh, OPM_2357), tf.equal(padmodh, OPM_NONE)], axis=0)
assert_pad_mode = tf.compat.v1.assert_greater(
tf.reduce_sum(tf.cast(flag_pad_mode, tf.int32)), 0,
message='Pad mode not valid', data=[padmodh])
flag_start_mode = tf.stack([tf.equal(smodeh, SMODE_CONSTANT), tf.equal(smodeh, SMODE_INPUT)], axis=0)
assert_start_mode = tf.compat.v1.assert_greater(
tf.reduce_sum(tf.cast(flag_start_mode, tf.int32)), 0,
message='Start mode not valid', data=[smodeh])
flag_shapes = tf.shape(datah) - tf.shape(kernh)
assert_shapes = tf.compat.v1.assert_greater_equal(
tf.reduce_sum(flag_shapes), 0,
message='Data shape must be >= kernel shape', data=[tf.shape(datah), tf.shape(kernh)])
with tf.control_dependencies([assert_pad_mode, assert_start_mode, assert_shapes]):
# If configured to do so, expand dimensions of data array to power of 2 or
# prime factor multiples (after adding a minimum padding as well, if given)
# to avoid use of Bluestein algorithm in favor of significantly faster Cooley-Tukey FFT
pad_shape = tf.shape(datah) + padminh
datat = tf.cond(tf.equal(padmodh, OPM_2357),
lambda: pad_around_center(datah, optimize_dims(pad_shape, OPM_2357), mode=self.pad_fill),
lambda: tf.cond(tf.equal(padmodh, OPM_LOG2),
lambda: pad_around_center(datah, optimize_dims(pad_shape, OPM_LOG2), mode=self.pad_fill),
lambda: pad_around_center(datah, pad_shape, mode=self.pad_fill)
))
# Pad kernel (with zeros only) to equal dimensions of data tensor and run "circular"
# transformation as this algorithm is based on circular convolutions and the results
# will have half spaces swapped otherwise
kernt = tf.cast(ifftshift(pad_around_center(kernh, tf.shape(datat))), self.fft_dtype)
# Infer available TF FFT functions based on predefined number of data dimensions
# TODO: Find a way to determine dimensionality of images separately from batch dimension and
# update the rank used to get fft fns excluding batch dim
fft_fwd, fft_rev = fft_utils_tf.get_fft_tf_fns(min(self.n_dims, 3), real_domain_only=self.real_domain_fft)
# Determine intermediate kernel representation necessary based on domain specified to
# carry out computations
kern_fft = fft_fwd(kernt)
if self.real_domain_fft:
kern_fft_conj = fft_fwd(tf.reverse(kernt, axis=tf.range(0, self.n_dims)))
else:
kern_fft_conj = tf.math.conj(kern_fft)
# Initialize resulting deconvolved image -- there are several sensible choices for this like the
# original image or constant arrays, but some experiments show this to be better, and other
# implementations doing the same are "Basic Matlab" and "Scikit-Image" (see class notes for links)
decon = tf.cond(
tf.equal(smodeh, SMODE_CONSTANT),
lambda: tf.identity(.5 * tf.ones_like(datat, dtype=self.dtype), name='deconvolution'),
# Multiplication used here to avoid https://github.com/tensorflow/tensorflow/issues/11186
lambda: tf.identity(datat * tf.ones_like(datat, dtype=self.dtype), name='deconvolution')
)
def cond(i, decon):
return i <= niter
def conv(data, kernel_fft):
return tf.math.real(fft_rev(fft_fwd(tf.cast(data, self.fft_dtype)) * kernel_fft))
def body(i, decon):
# Richardson-Lucy Iteration - logic taken largely from a combination of
# the scikit-image (real domain) and DeconvolutionLab2 implementations (complex domain)
conv1 = conv(decon, kern_fft)
# High-pass filter to avoid division by very small numbers (see DeconvolutionLab2)
blur1 = tf.where(conv1 < self.epsilon, tf.zeros_like(datat), datat / conv1, name='blur1')
conv2 = conv(blur1, kern_fft_conj)
# Positivity constraint on result for iteration
decon = tf.maximum(decon * conv2, 0.)
# If given an "observer", pass the current image restoration and iteration counter to it
if self.observer_fn is not None:
# Remove any cropping that may have been added as this is usually not desirable in observers
decon_crop = unpad_around_center(decon, tf.shape(datah))
_, i, decon = tf_observer([decon_crop, i, decon], self.observer_fn)
return i + 1, decon
result = tf.while_loop(cond, body, [1, decon], parallel_iterations=1)[1]
# Crop off any padding that may have been added to reach more efficient dimension sizes
result = unpad_around_center(result, tf.shape(datah))
# Wrap output in configured post-processing functions (if any)
result = tf.identity(self._wrap_output(result, {'data': datah, 'kernel': kernh}), name='result')
inputs = {
'niter': niter, 'data': dataph, 'kernel': kernph,
'pad_mode': padmodh, 'pad_min': padminh, 'start_mode': smodeh
}
outputs = {
'result': result,
'data_shape': tf.shape(datah), 'kern_shape': tf.shape(kernh),
'pad_shape': pad_shape, 'pad_mode': padmodh,
'datat_shape': tf.shape(datat),
'pad_min': padminh, 'start_mode': smodeh,
}
return inputs, outputs
def _build_tf_graph(self):
niter = self._get_niter()
# Create argument placeholders with same defaults as those used at graph construction time
padmodh = tf.compat.v1.placeholder_with_default(DEFAULT_PAD_MODE, (),
name='pad_mode')
smodeh = tf.compat.v1.placeholder_with_default(DEFAULT_START_MODE, (),
name='start_mode')
padminh = tf.compat.v1.placeholder_with_default(tf.zeros(
self.n_dims, dtype=tf.int32),
self.n_dims,
name='pad_min')
# Data and kernel should have shapes (z, height, width)
dataph = tf.compat.v1.placeholder(self.dtype,
shape=[None] * self.n_dims,
name='data')
kernph = tf.compat.v1.placeholder(self.dtype,
shape=[None] * self.n_dims,
name='kernel')
datah, kernh = self._wrap_input(dataph), self._wrap_input(kernph)
# Add assertion operations to validate padding mode, start mode, and data/kernel dimensions
flag_pad_mode = tf.stack([
tf.equal(padmodh, OPM_LOG2),
tf.equal(padmodh, OPM_2357),
tf.equal(padmodh, OPM_NONE)
],
axis=0)
assert_pad_mode = tf.compat.v1.assert_greater(
tf.reduce_sum(tf.cast(flag_pad_mode, tf.int32)),
0,
message='Pad mode not valid',
data=[padmodh])
flag_start_mode = tf.stack(
[tf.equal(smodeh, SMODE_CONSTANT),
tf.equal(smodeh, SMODE_INPUT)],
axis=0)
assert_start_mode = tf.compat.v1.assert_greater(
tf.reduce_sum(tf.cast(flag_start_mode, tf.int32)),
0,
message='Start mode not valid',
data=[smodeh])
flag_shapes = tf.shape(datah) - tf.shape(kernh)
assert_shapes = tf.compat.v1.assert_greater_equal(
tf.reduce_sum(flag_shapes),
0,
message='Data shape must be >= kernel shape',
data=[tf.shape(datah), tf.shape(kernh)])
with tf.control_dependencies(
[assert_pad_mode, assert_start_mode, assert_shapes]):
# If configured to do so, expand dimensions of data array to power of 2 or
# prime factor multiples (after adding a minimum padding as well, if given)
# to avoid use of Bluestein algorithm in favor of significantly faster Cooley-Tukey FFT
pad_shape = tf.shape(datah) + padminh
datat = tf.cond(
tf.equal(padmodh, OPM_2357),
lambda: pad_around_center(datah,
optimize_dims(pad_shape, OPM_2357),
mode=self.pad_fill),
lambda: tf.cond(
tf.equal(padmodh, OPM_LOG2), lambda: pad_around_center(
datah,
optimize_dims(pad_shape, OPM_LOG2),
mode=self.pad_fill), lambda: pad_around_center(
datah, pad_shape, mode=self.pad_fill)))
# Pad kernel (with zeros only) to equal dimensions of data tensor and run "circular"
# transformation as this algorithm is based on circular convolutions and the results
# will have half spaces swapped otherwise
kernt = tf.cast(
ifftshift(pad_around_center(kernh, tf.shape(datat))),
self.fft_dtype)
# Infer available TF FFT functions based on predefined number of data dimensions
# TODO: Find a way to determine dimensionality of images separately from batch dimension and
# update the rank used to get fft fns excluding batch dim
fft_fwd, fft_rev = fft_utils_tf.get_fft_tf_fns(
min(self.n_dims, 3), real_domain_only=self.real_domain_fft)
# Determine intermediate kernel representation necessary based on domain specified to
# carry out computations
kern_fft = fft_fwd(kernt)
if self.real_domain_fft:
kern_fft_conj = fft_fwd(
tf.reverse(kernt, axis=tf.range(0, self.n_dims)))
else:
kern_fft_conj = tf.math.conj(kern_fft)
# Initialize resulting deconvolved image -- there are several sensible choices for this like the
# original image or constant arrays, but some experiments show this to be better, and other
# implementations doing the same are "Basic Matlab" and "Scikit-Image" (see class notes for links)
decon = tf.cond(
tf.equal(smodeh, SMODE_CONSTANT),
lambda: tf.identity(.5 * tf.ones_like(datat, dtype=self.dtype),
name='deconvolution'),
# Multiplication used here to avoid https://github.com/tensorflow/tensorflow/issues/11186
lambda: tf.identity(datat * tf.ones_like(datat, dtype=self.dtype),
name='deconvolution'))
def cond(i, decon):
return i <= niter
def conv(inputData, kernel_fft):
return tf.math.real(
fft_rev(
fft_fwd(tf.cast(inputData, self.fft_dtype)) * kernel_fft))
def gaussian_kernel(
size: int,
mean: float,
std: float,
):
"""Makes 3D gaussian Kernel for convolution."""
d = tf.distributions.Normal(mean, std)
vals = d.prob(
tf.range(start=-size, limit=size + 1, dtype=tf.float32))
gauss_kernel = tf.einsum('i,j,k->ijk', vals, vals, vals)
# return the kernel normalised to sum =1
return gauss_kernel / tf.reduce_sum(gauss_kernel)
gaussKernel = gaussian_kernel(9, 1.0, 7.0)
# Expand dimensions of `gauss_kernel` for `tf.nn.conv3d` signature.
gaussKernel = gaussKernel[:, :, :, tf.newaxis, tf.newaxis, tf.newaxis]
def body(
i,
decon,
):
'''# Richardson-Lucy Iteration - logic taken largely from a combination of
# the scikit-image (real domain) and DeconvolutionLab2 implementations (complex domain)
# conv1 is the current model blurred with the PSF
conv1 = conv(decon, kern_fft)
# High-pass filter to avoid division by very small numbers (see DeconvolutionLab2)
blur1 = tf.where(conv1 < self.epsilon, tf.zeros_like(datat), datat / conv1, name='blur1')
# conv2 is the blurred model convolved with the flipped PSF
conv2 = conv(blur1, kern_fft_conj)
# Positivity constraint on result for iteration
decon = tf.maximum(decon * conv2, 0.)
'''
# Gold algorithm, ratio method, simpler then RL, doesnt use flipped OTF
# conv1 is the current model blurred with the PSF
conv1 = conv(decon, kern_fft)
# High-pass filter to avoid division by very small numbers (see DeconvolutionLab2)?
# we wont do it here as we will use the delta parameter in denom and numerrator of division to get blur2
# as per Stephan Ludwig et al 2019
# should normalise blur2 and decon each time because numbers get big and we risk overflow when multiplying in next step
conv1norm = conv1 / (tf.math.reduce_sum(conv1))
datatNorm = datat / (tf.math.reduce_sum(datat))
# this value seems to work well fo rthe images that are normalised to sum of 1
deltaParam = 1e-4
ratio = (datatNorm + deltaParam) / (conv1norm + deltaParam)
#blur1 = tf.where(conv1 < self.epsilon, tf.zeros_like(datat), datat / conv1, name='blur1')
#ratioNorm = ratio / (tf.math.reduce_sum(ratio))
#deconNorm = decon / (tf.math.reduce_sum(decon))
# decon is the normalised blurred model multiplied by the model
# Positivity constraint on result for iteration
decon = tf.maximum(decon * ratio, 0.)
# Smooth the intermediate result image with Gaussian of sigma 1 every 5th iteration
# to control noise buildup that Gold method is succeptible to.
# Use tf.nn.conv3d to convolve a Gaussian kernel with an image:
# Make Gaussian Kernel with desired specs using gaussian_kernel function defined above
if i % 5 == 0:
# Convolve decon with gauss kernel.
tf.nn.conv3d(decon,
filter=gaussKernel,
strides=[1, 1, 1, 1, 1],
padding="SAME")
# normalise the result so the sum of the data is 1
decon = decon / (tf.math.reduce_sum(decon))
# TODO - Smoothing every 5 iterations with gaussian or wiener.
# TODO rescale back to input data sum intensity - probably need to adjust deltaParam too.
# If given an "observer", pass the current image restoration and iteration counter to it
if self.observer_fn is not None:
# Remove any cropping that may have been added as this is usually not desirable in observers
decon_crop = unpad_around_center(decon, tf.shape(datah))
# normalise the result so the sum of the data is 1
decon_crop = decon_crop / (tf.math.reduce_sum(decon_crop))
# we can use these captured observed tensors to evaluate eg convergence
# in eg. the observer function used.
_, i, decon, conv1 = tf_observer([decon_crop, i, decon, conv1],
self.observer_fn)
return i + 1, decon
result = tf.while_loop(cond, body, [1, decon],
parallel_iterations=1)[1]
# Crop off any padding that may have been added to reach more efficient dimension sizes
result = unpad_around_center(result, tf.shape(datah))
# Wrap output in configured post-processing functions (if any)
result = tf.identity(self._wrap_output(result, {
'data': datah,
'kernel': kernh
}),
name='result')
inputs = {
'niter': niter,
'data': dataph,
'kernel': kernph,
'pad_mode': padmodh,
'pad_min': padminh,
'start_mode': smodeh
}
outputs = {
'result': result,
'data_shape': tf.shape(datah),
'kern_shape': tf.shape(kernh),
'pad_shape': pad_shape,
'pad_mode': padmodh,
'datat_shape': tf.shape(datat),
'pad_min': padminh,
'start_mode': smodeh,
}
return inputs, outputs
