本代码绝大部分来自于 An Analysis and Implementation of the FFDNet Image Denoising Method ，只是不知道为何其中计算损失的时候使是将输出与噪声进行计算，所以该代码其实训练的是生成噪声，然后用带噪声的图像减去噪声，从而得到干净的图像，但是 FFDNet 原文不是这样子的，所以我就做了一点点修改。

FFDNet 的基本介绍和代码的基本使用我在 这里 有个简单的介绍，不足的地方，要是有人看到了，请提交issue。

% A PyTorch implementation of FFDNet image denoising.

ABOUT

• Author : Matias Tassano [email protected]
• Copyright : (C) 2018 IPOL Image Processing On Line http://www.ipol.im/
• Licence : GPL v3+, see GPLv3.txt

OVERVIEW

This source code provides a PyTorch implementation of FFDNet image denoising, as in Zhang, Kai, Wangmeng Zuo, and Lei Zhang. "FFDNet: Toward a fast and flexible solution for CNN based image denoising." arXiv preprint arXiv:1710.04026 (2017).

USER GUIDE

The code as is runs in Python 3.6 with the following dependencies:

Dependencies

• PyTorch v0.3.1
• scikit-image
• torchvision
• OpenCV
• HDF5
• tensorboardX

Usage

1. Testing

If you want to denoise an image using a one of the pretrained models found under the models folder you can execute

python test_ffdnet_ipol.py \
	--input input.png \
	--noise_sigma 25 \
	--add_noise True

To run the algorithm on CPU instead of GPU:

python test_ffdnet_ipol.py \
	--input input.png \
	--noise_sigma 25 \
	--add_noise True \
	--no_gpu

NOTES

• Models have been trained for values of noise in [0, 75]
• add_noise can be set to False if the input image is already noisy

2. Training

Prepare the databases

First, you will need to prepare the dataset composed of patches by executing prepare_patches.py indicating the paths to the directories containing the training and validation datasets by passing --trainset_dir and --valset_dir, respectively.

Image datasets are not provided with this code, but the following can be downloaded Training: Waterloo Exploration Database Validation: Kodak Lossless True Color Image Suite

NOTES

• To prepare a grayscale dataset: python prepare_patches.py --gray
• --max_number_patches can be used to set the maximum number of patches contained in the database

Train a model

A model can be trained after having built the training and validation databases (i.e. train_rgb.h5 and val_rgb.h5 for color denoising, and train_gray.h5 and val_gray.h5 for grayscale denoising). Only training on GPU is supported.

python train.py \
	--batch_size 128 \
	--epochs 80 \
	--noiseIntL 0 75
	--val_noiseL 25

NOTES

• The training process can be monitored with TensorBoard as logs get saved in the log_dir folder
• By default, models are trained for values of noise in [0, 75] (--noiseIntL flag)
• By default, noise added at validation is set to 20 (--val_noiseL flag)
• A previous training can be resumed passing the --resume_training flag

ABOUT THIS FILE

Copyright 2018 IPOL Image Processing On Line http://www.ipol.im/

Copying and distribution of this file, with or without modification, are permitted in any medium without royalty provided the copyright notice and this notice are preserved. This file is offered as-is, without any warranty.

ACKNOLEDGMENTS

Some of the code is based on code by Yiqi Yan [email protected]