<p align="center"> <img src="https://github.com/lime-j/RDNet/blob/main/figures/Title.png?raw=true" width=95%> <p>

Reversible Decoupling Network for Single Image Reflection Removal

<div align="center"> </div> <p align="center" style="font-size: larger;"> <a href="https://arxiv.org/abs/2410.08063"> Reversible Decoupling Network for Single Image Reflection Removal</a> </p> <p align="center"> <a href="https://github.com/WHiTEWoLFJ"> Hao Zhao</a> ⚔️, <a href="https://github.com/lime-j"> Mingjia Li</a> ⚔️, <a href="https://github.com/mingcv"> Qiming Hu</a>, <a href="https://sites.google.com/view/xjguo"> Xiaojie Guo</a> 🦅, <p align="center">(⚔️: equal contribution, 🦅 : corresponding author)</p> </p> <p align="center"> <img src="https://github.com/lime-j/RDNet/blob/main/figures/net.png?raw=true" width=95%> <p> <details> <summary>Click for the Abstract of RDNet</summary> We present a Reversible Decoupling Network (RDNet), which employs a reversible encoder to secure valuable information while flexibly decoupling transmission-and-reflection-relevant features during the forward pass. Furthermore, we customize a transmission-rate-aware prompt generator to dynamically calibrate features, further boosting performance. Extensive experiments demonstrate the superiority of RDNet over existing SOTA methods on five widely-adopted benchmark datasets. </details>

Our work is accepted by CVPR 2025! See you at the conference!

Our work RDNet wins the NTIRE 2025 Single Image Reflection Removal in the Wild Challenge!

This codebase is deprecated. Please use XReflection(https://github.com/hainuo-wang/XReflection) instead! It supports multi-GPU training and has better reproducibility.

🌠 Gallery

<table class="center"> <tr> <td><p style="text-align: center">Class Room</p></td> <td><p style="text-align: center">White Wall Chamber</p></td> </tr> <tr> <td> <div style="width: 100%; max-width: 600px; position: relative;"> <img src="https://github.com/lime-j/RDNet/blob/main/figures/Input_class.png?raw=true" style="width: 100%; height: 300px; display: block;"> <img src="https://github.com/lime-j/RDNet/blob/main/figures/Ours_class.png?raw=true" style="width: 100%; height: 300px; display: block; position: absolute; top: 0; left: 0; opacity: 0; transition: opacity 0.5s;" onmouseover="this.style.opacity=1;" onmouseout="this.style.opacity=0;"> </div> </td> <td> <div style="width: 100%; max-width: 600px; position: relative;"> <img src="https://github.com/lime-j/RDNet/blob/main/figures/input_white.jpg?raw=true" style="width: 100%; height: 300px; display: block;"> <img src="https://github.com/lime-j/RDNet/blob/main/figures/Ours_white.png?raw=true" style="width: 100%; height: 300px; display: block; position: absolute; top: 0; left: 0; opacity: 0; transition: opacity 0.5s;" onmouseover="this.style.opacity=1;" onmouseout="this.style.opacity=0;"> </div> </td> </tr> <tr> <td><p style="text-align: center">Car Window</p></td> <td><p style="text-align: center">Very Green Office</p></td> </tr> <tr> <td> <div style="width: 100%; max-width: 600px; position: relative;"> <img src="https://github.com/lime-j/RDNet/blob/main/figures/Input_car.jpg?raw=true" style="width: 100%; height: 300px; display: block;"> <img src="https://github.com/lime-j/RDNet/blob/main/figures/Ours_car.png?raw=true" style="width: 100%; height: 300px; display: block; position: absolute; top: 0; left: 0; opacity: 0; transition: opacity 0.5s;" onmouseover="this.style.opacity=1;" onmouseout="this.style.opacity=0;"> </div> </td> <td> <div style="width: 100%; max-width: 600px; position: relative;"> <img src="https://github.com/lime-j/RDNet/blob/main/figures/Input_green.png?raw=true" style="width: 100%; height: 300px; display: block;"> <img src="https://github.com/lime-j/RDNet/blob/main/figures/Ours_green.png?raw=true" style="width: 100%; height: 300px; display: block; position: absolute; top: 0; left: 0; opacity: 0; transition: opacity 0.5s;" onmouseover="this.style.opacity=1;" onmouseout="this.style.opacity=0;"> </div> </td> </tr> </table>

Requirements

We recommend torch 2.x for our code, but it should works fine with most of the modern versions.

pip install torch>=2.0 torchvision
pip install einops ema-pytorch fsspec fvcore huggingface-hub matplotlib numpy opencv-python omegaconf pytorch-msssim scikit-image scikit-learn scipy tensorboard tensorboardx wandb timm

Testing

The checkpoint for the main network is available at https://checkpoints.mingjia.li/rdnet.pth ; while the model for cls_model is at https://checkpoints.mingjia.li/cls_model.pth . Please put the cls_model.pth under "pretrained" folder.

python3 test_sirs.py --icnn_path <path to the main checkpoint> --resume

Training

The training script / data preprocessing script is released. Just use train.py to train the model. The pretrained focalnet backbone is in https://checkpoints.mingjia.li/focal.pth

python3 train.py 

Acknowledgement

We are grateful for the computational resource support provided by Google's TPU Research Cloud and DataCanvas Limited.