# Copyright 2014 Diamond Light Source Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
.. module:: ring_removal_regularization
:platform: Unix
:synopsis: Method working in the sinogram space to remove ring artifacts.
.. moduleauthor:: Nghia Vo <scientificsoftware@diamond.ac.uk>
"""
from savu.plugins.plugin import Plugin
from savu.plugins.driver.cpu_plugin import CpuPlugin
from savu.plugins.utils import register_plugin
import numpy as np
[docs]@register_plugin
class RingRemovalRegularization(Plugin, CpuPlugin):
def __init__(self):
super(RingRemovalRegularization, self).__init__(
"RingRemovalRegularization")
[docs] def setup(self):
in_dataset, out_dataset = self.get_datasets()
out_dataset[0].create_dataset(in_dataset[0])
in_pData, out_pData = self.get_plugin_datasets()
in_pData[0].plugin_data_setup('SINOGRAM', 'single')
out_pData[0].plugin_data_setup('SINOGRAM', 'single')
[docs] def pre_process(self):
in_pData = self.get_plugin_in_datasets()
width_dim = in_pData[0].get_data_dimension_by_axis_label('detector_x')
height_dim = in_pData[0].get_data_dimension_by_axis_label(
'rotation_angle')
sino_shape = list(in_pData[0].get_shape())
self.width1 = sino_shape[width_dim]
self.height1 = sino_shape[height_dim]
alpha = self.parameters['alpha']
tau = 2.0 * np.arcsinh(np.sqrt(alpha) * 0.5)
ilist = np.arange(0, self.width1)
jlist = np.arange(0, self.width1)
matjj, matii = np.meshgrid(jlist, ilist)
mat1 = np.abs(matii - matjj)
mat2 = matii + matjj
mat1a = np.cosh((self.width1 - 1 - mat1) * tau)
mat2a = np.cosh((self.width1 - mat2) * tau)
self.mat_coe = -(np.tanh(0.5 * tau) /
(alpha * np.sinh(self.width1 * tau))) * (mat1a + mat2a)
[docs] def process_frames(self, data):
sinogram = np.copy(data[0])
num_mean = np.mean(sinogram)
sinogram[sinogram <= 0.0] = num_mean # Note performance
sinogram = -np.log(sinogram)
num_chunks = np.clip(
np.int16(self.parameters['number_of_chunks']), 1, self.height1)
list_pos = np.array_split(np.arange(self.height1), num_chunks)
list_grad = np.zeros(self.width1, dtype=np.float32)
mat_grad = np.zeros((self.width1, self.width1), dtype=np.float32)
for pos in list_pos:
bindex = pos[0]
eindex = pos[-1] + 1
list_mean = np.mean(sinogram[bindex:eindex, :], axis=0)
list_grad[1:-1] = - np.diff(list_mean, 2)
list_grad[0] = list_mean[0] - list_mean[1]
list_grad[-1] = list_mean[-1] - list_mean[-2]
mat_grad[:] = list_grad
list_corr = np.sum(mat_grad * self.mat_coe, axis=1)
mat_corr = np.zeros(
(eindex - bindex, self.width1), dtype=np.float32)
mat_corr[:] = list_corr
sinogram[bindex:eindex, :] = sinogram[bindex:eindex, :] + mat_corr
return np.exp(-sinogram)