# Copyright or © or Copr. IETR/INSA Rennes (2019)
#
# Contributors :
# Eduardo Fernandes-Montesuma eduardo.fernandes-montesuma@insa-rennes.fr (2019)
# Florian Lemarchand florian.lemarchand@insa-rennes.fr (2019)
#
#
# OpenDenoising is a computer program whose purpose is to benchmark image
# restoration algorithms.
#
# This software is governed by the CeCILL-C license under French law and
# abiding by the rules of distribution of free software. You can use,
# modify and/ or redistribute the software under the terms of the CeCILL-C
# license as circulated by CEA, CNRS and INRIA at the following URL
# "http://www.cecill.info".
#
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import torch
import torch.nn as nn
[docs]class DnCNN(nn.Module):
[docs] def __init__(self, depth=17, n_filters=64, kernel_size=3, n_channels=1):
"""Pytorch implementation of DnCNN. Implementation followed the original paper [1]_. Authors original code can be
found on `their Github Page
<https://github.com/cszn/DnCNN/>`_.
Notes
-----
This implementation is based on the following `Github page
<https://github.com/SaoYan/DnCNN-PyTorch>`_.
Parameters
----------
depth : int
Number of fully convolutional layers in dncnn. In the original paper, the authors have used depth=17 for non-
blind denoising and depth=20 for blind denoising.
n_filters : int
Number of filters on each convolutional layer.
kernel_size : int tuple
2D Tuple specifying the size of the kernel window used to compute activations.
n_channels : int
Number of image channels that the network processes (1 for grayscale, 3 for RGB)
References
----------
.. [1] Zhang K, Zuo W, Chen Y, Meng D, Zhang L. Beyond a gaussian denoiser: Residual learning of deep cnn
for image denoising. IEEE Transactions on Image Processing. 2017
Example
-------
>>> from OpenDenoising.model.architectures.pytorch import DnCNN
>>> dncnn_s = DnCNN(depth=17)
>>> dncnn_b = DnCNN(depth=20)
"""
super(DnCNN, self).__init__()
layers = [
nn.Conv2d(in_channels=n_channels, out_channels=n_filters, kernel_size=kernel_size,
padding=1, bias=False),
nn.ReLU(inplace=True)
]
for _ in range(depth-2):
layers.append(nn.Conv2d(in_channels=n_filters, out_channels=n_filters, kernel_size=kernel_size,
padding=1, bias=False))
layers.append(nn.BatchNorm2d(n_filters))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.Conv2d(in_channels=n_filters, out_channels=n_channels, kernel_size=kernel_size,
padding=1, bias=False))
self.dncnn = nn.Sequential(*layers)
[docs] def forward(self, x):
out = self.dncnn(x)
return out