Awesome

ReCoNet

Zhanbo Huang, Jinyuan Liu, Xin Fan*, Risheng Liu, Wei Zhong, Zhongxuan Luo. "Recurrent Correction Network for Fast and Efficient Multi-modality Image Fusion", European Conference on Computer Vision (ECCV), 2022.

Milestone

In the near future, we will publish the following materials.

• v0 [ECCV]: Fuse network (ReCo) with pre-trained parameters for generating results in paper. Finished
• v1: A new script & architecture of ReCo+ for fast training & prediction. Building
• v1: A highly robust pre-trained parameters for ReCo+ based on realistic scene training. (We are collecting data with realistic implications.)

Update

[2022-07-15] Train script for ReCo(v1) is available!

[2022-07-13] Preview of micro-register is available!

[2022-07-12] The ReCo(v0) is available!

Requirements

• Python 3.10
• PyTorch 1.12
• TorchVision 0.13.0
• PyTorch lightning 0.8.5
• Kornia 0.6.5

Extended Experiments

Generate fake visible images

To generating fake visible images as described in our paper, you can refer to my another repository complex-deformation, which is a component of this work.

It shows how we can deform the image and generate a restored field that approximates the ground truth.

Have a quick preview of our micro-register

To give a quick preview of our micro-register module, you can try the training & prediction based on the MNIST dataset.

Activate your conda environment and enter folder exp/test_register.

1. To train the register yourself, you just need to run this code.

export PYTHONPATH="${PYTHONPATH}:$RECO_ROOT"
python test_register.py --backbone $BACKBONE --dst $DST

The $RECO_ROOT is the root path of ReCo repository, like ~/lab/reco, the $BACKBONE denotes which architecture to use m-micro or u-unet.

We will do following things automatically: download MNIST dataset, train the register, and save predictions in $DST.

2. If you just want to test the performance, we offer pre-trained parameters for both micro and unet based register.

export PYTHONPATH="${PYTHONPATH}:$RECO_ROOT"
python test_register.py --backbone $BACKBONE --dst $DST --only_pred

The prediction results will be save in $DST and the patches from left to right are moving, fixed and moved, respectively.

Get start (v0) (Recommended for Now)

1. To use our pre-trained parameters of ECCV-22 for fusion, you need to prepare your dataset in $ROOT/data/$NAME.

  $DATA (dataset name, like: tno)
  ├── ir
  ├── vi

2. Enter the archive folder cd archive, and activate your conda environment conda activate $CONDA_ENV.

export PYTHONPATH="${PYTHONPATH}:$RECO_ROOT"
python fuse.py --ir ../data/$DATA/ir --vi ../data/$DATA/vi --dst $SAVE_TO_WHERE 

3. Now, you will find the fusion results in $SAVE_TO_WHERE, this operation will create output folder automatically.

Get start (v1) Preview Version

Only recommended if you are intending in training ReCo+ yourself.

Note that: Due to the instability of the micro-register module in the future, we recommend training only the fusion part.

1. To use the script to train ReCo+ yourself, you need to prepare your dataset in $ROOT/data/$NAME.

  $DATA (dataset name, like: tno)
  ├── ir
  ├── vi
  ├── iqa (new for v1, optional)
  |   |   ├── ir (information measurement for infrared images)
  |   |   ├── vi (information measurement for visible images)
  ├── meta (new for v1)
  |   |   ├── train.txt (which images are used for training)
  |   |   ├── val.txt (which images are used for validation)
  |   |   ├── pred.txt (which images are used for prediction)

2. Activate your conda environment conda activate $CONDA_ENV.

# set project path for python
export PYTHONPATH="${PYTHONPATH}:$RECO_ROOT"
# only train fuse part (ReCo) **current recommended**
python train.py --data data/$DATA --ckpt $CHECKPOINT_PATH --lr 1e-3
# train registration and fuse (ReCo+)
python train.py --register m --data data/$DATA --ckpt $CHECKPOINT_PATH --lr 1e-3 --deform $DEFORM_LEVEL

The $DEFORM_LEVEL should be easy, normal or hard.

⚠️ Limitations: As mentioned in the paper, when the difference between mid-wave infrared and visible images in your dataset is too large, the register may not converge properly.

3. To generate the fusion images with pre-trained parameters, just run the following.

# set project path for python
export PYTHONPATH="${PYTHONPATH}:$RECO_ROOT"
# only fuse part (ReCo) **current recommended**
python pred.py --data $data/$DATA --ckpt $CHECKPOINT_PATH --dst $SAVE_TO_WHERE
# registration & fuse (ReCo+)
 python pred.py --register m --data $data/$DATA --ckpt $CHECKPOINT_PATH --dst $SAVE_TO_WHERE