# -*- coding: utf-8 -*-
# Copyright 2020 United Kingdom Research and Innovation
# Copyright 2020 The University of Manchester
#
# 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.
#
# Authors:
# CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
from cil.optimisation.functions import L2NormSquared, L1Norm
import numpy as np
[docs]def mse(dc1, dc2):
''' Returns the Mean Squared error of two DataContainers
'''
diff = dc1 - dc2
return L2NormSquared().__call__(diff)/dc1.size
[docs]def mae(dc1, dc2):
''' Returns the Mean Absolute error of two DataContainers
'''
diff = dc1 - dc2
return L1Norm().__call__(diff)/dc1.size
[docs]def psnr(ground_truth, corrupted, data_range = 255):
''' Returns the Peak signal to noise ratio
'''
tmp_mse = mse(ground_truth, corrupted)
if tmp_mse == 0:
return 1e5
return 10 * np.log10((data_range ** 2) / tmp_mse)