Image restoration still poses significant challenges due to the complexity and diversity of real-world degradations. However, such degradations often manifest as spatially non-uniform, locally concentrated, and irregular patterns, which calls for a restoration model that can adapt its feature sampling to the underlying degradation geometry while remaining computationally efficient. In this paper, we design a novel image restoration framework built on a deformable restoration Transformer, termed DeRestormer. DeRestormer adapts to irregular but localized degradations via deformable attention, where a small set of sampling positions is learned to pre-filter and highlight key elements, so the model can better focus on informative regions and handle complex degradation patterns efficiently. In addition, we incorporate deformable convolutions at the bottleneck stage to dynamically adjust sampling locations across different scales, thus facilitating flexible multi-scale aggregation and effective integration of fine-grained details and global context. Extensive experiments on 13 datasets across 4 tasks demonstrate the effectiveness of DeRestormer, since it achieves state-of-the-art performance across a wide range of image restoration scenarios. The project and pretrained models of our work are available at https://github.com/Kingsley-Cyl/DeRestormer.

Installation

This code was tested with the following environment configurations. It may work with other versions.

• CUDA 11.8
• Python 3.8
• PyTorch 2.1.0+cu118

You can manage your virtual environment with conda by conducting the following commands:

conda create -n DeRestormer python=3.8
conda activate DeRestormer
cd <this repository>
pip install -r requirements.txt

cd pytorch-gradual-warmup-lr
python setup.py install
cd ..

Pretrained Weights

You can find and download all the pretrained Weights at Baidu Netdisk (Password: t6w4).

Training

The training code will be updated soon.

Testing

The testing code will be updated soon.

1. for DID-Data and DDN-Data datasets: PSNR and SSIM results are computed by using this Matlab Code.
2. for other datasets: PSNR and SSIM results are computed by using this Matlab Code.

Image deraining

Image desnowing

Image dehazing

Image motion deblurring

This code is based on MSDT, NeRD-Rain, and DeformDeweatherNet. Thanks for their awesome work.