[ICCP'22] Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline
By Guocheng Qian, Yuanhao Wang, Chao Dong, Jimmy S. Ren, Wolfgang Heidrich, Bernard Ghanem, Jinjin Gu
The original name of this project is: "Trinity of Pixel Enhancement: a Joint Solution for Demosaicking, Denoising and Super-Resolution"
We insert the proposed pipeline DN -> SR -> DM into an end-to-end network constructed by RRDB for the joint DN, DM and SR. We leverage the detachable branch to provide the middle stage supervision.
Will be available soon.
Clone this github repo and install the environment by:
git clone https://github.com/guochengqian/TENet
cd TENet
source env_install.sh
conda activate tenet
synthetic data preparation
Download (DIV2K) dataset
create data directory in TENet folder: mkdir data && cd data
Link DIV2K data into ./data/DIV2K, e.g. ln -s /data/lowlevel/DIV2K ./
Crop DIV2K
cd ../datasets
python crop_imgs.py --src_dir ../data/DIV2K/DIV2K_val5_HR --save_dir ../data/DIV2K/DIV2K_val5_HR_sub # crop val5 images
python generate_datalist_div2k.py # generate div2k training and val dataset
The generated .txt file train_div2k.txt
and val_div2k.txt
are used for training on DIV2K.
PixelShift200 data preparation
Download [Pixelshift200]. They are .mat format, having 4 channels (R, Gr, Gb, B). Unzip the .zip file and put all folders inside into one folder called pixelshift200. For example, put here /data/pixelshift200
.
Link PixelShift200 data into ./data/pixelshift200, e.g. cd TENet/data && ln -s /data/pixelshift200 pixelshift200
Crop images into 512*512, and generate the text file contains the location of each image:
cd ../datasets
python crop_pixelshift200.py
python generate_datalist_pixelshift.py
The generated .txt file train_pixelshift.txt
(9444 Lines) and val_pixelshift.txt
(20 Lines) are used for training. check them.
DN+DM+SR (end to end without pipeline)
python train.py --in_type noisy_lr_raw --mid_type None --out_type linrgb --model tenet --train_list datasets/train_pixelshift.txt --val_list datasets/val_pixelshift.txt --n_gpus 4 --use_wandb --block_type rrdb --n_blocks 6 --imgs_per_gpu 8
DN+SR->DM (our TENet)
python train.py --in_type noisy_lr_raw --mid_type raw --out_type linrgb --model tenet --train_list datasets/train_pixelshift.txt --val_list datasets/val_pixelshift.txt --n_gpus 4 --use_wandb --block_type rrdb --n_blocks 6 --imgs_per_gpu 8
Note:
--mid_type raw
is to activate the auxiliary mid stage supervision. Here we add raw
as the supervision, therefore, the pipeline will be DN+SR->DM.
for training on a different dataset, like DIV2K, change --train_list datasets/train_div2k.txt --val_list datasets/val_div2k.txt
for using a different building block, such as NLSA --block_type nlsa
, or EAM --block_type eam
, or RRG --block_type rrg
, or DRLM --block_type drlm
or RRDB --block_type rrdb
Monitor your jobs using Tensorboard (log saved in ./log folder by default, tensorboard --logdir=./
) or using wandb (online website) by set --use_wandb
.
# RawDN
python train.py --in_type noisy_raw --out_type raw --model resnet --train_list datasets/train_pixelshift.txt --val_list datasets/val_pixelshift.txt --n_gpus 4 --use_wandb --block_type rrdb --n_blocks 6
# RawSR
python train.py --in_type lr_raw --out_type raw --model resnet --train_list datasets/train_pixelshift.txt --val_list datasets/val_pixelshift.txt --n_gpus 4 --use_wandb --block_type rrdb --n_blocks 6
# DM
python train.py --in_type raw --out_type linrgb --model resnet --train_list datasets/train_pixelshift.txt --val_list datasets/val_pixelshift.txt --n_gpus 4 --use_wandb --block_type rrdb --n_blocks 6
# RGBDN
python train.py --in_type noisy_linrgb --out_type linrgb --model resnet --train_list datasets/train_pixelshift.txt --val_list datasets/val_pixelshift.txt --n_gpus 4 --use_wandb --block_type rrdb --n_blocks 6
# RGBSR
python train.py --in_type lr_linrgb --out_type linrgb --model resnet --train_list datasets/train_pixelshift.txt --val_list datasets/val_pixelshift.txt --n_gpus 4 --use_wandb --block_type rrdb --n_blocks 6
JDSR
python train.py --in_type noisy_lr_raw --mid_type None --out_type linrgb --model jdsr --train_list datasets/train_pixelshift.txt --val_list datasets/val_pixelshift.txt --n_gpus 4 --use_wandb --block_type res --n_blocks 12 --channels 256
JDnDmSR
python train.py --in_type noisy_lr_raw --mid_type lr_raw --out_type linrgb --model jdndmsr --scale 2 --train_list datasets/train_pixelshift.txt --val_list datasets/val_pixelshift.txt --n_gpus 4 --n_blocks 2 --block rcab
bash script_all_pipelines.sh
Note:
Please cite the following paper if you feel TENet is useful to your research
@article{qian2019trinity,
title={Rethink the Pipeline of Demosaicking, Denoising, and Super-resolution},
author={Qian, Guocheng and Wang, Yuanhao and Dong, Chao and Ren, Jimmy S and Heidrich, Wolfgang and Ghanem, Bernard and Gu, Jinjin},
journal={arXiv preprint arXiv:1905.02538},
year={2019}
}