Image restoration aims to recover images from spatially-varying degradation. Most existing image-restoration models employed static CNN-based models, where the fixed learned filters cannot fit the diverse degradation well. To address this, in this paper, we propose a novel Dynamic Image Restoration Contrastive Network (DRCNet). The principal block in DRCNet is the Dynamic Filter Restoration module (DFR), which mainly consists of the spatial filter branch and the energy-based attention branch. Specifically, the spatial filter branch suppresses spatial noise for varying spatial degradation; the energy-based attention branch guides the feature integration for better spatial detail recovery. To make degraded images and clean images more distinctive in the representation space, we develop a novel Intra-class Contrastive Regularization (Intra-CR) to serve as a constraint in the solution space for DRCNet. Meanwhile, our theoretical derivation proved Intra-CR owns less sensitivity towards hyper-parameter selection than previous contrastive regularization. DRCNet achieves state-of-the-art results on the ten widely-used benchmarks in image restoration. Besides, we conduct ablation studies to show the effectiveness of the DFR module and Intra-CR, respectively.
This implementation based on BasicSR which is a open source toolbox for image/video restoration tasks.
python 3.8.10
pytorch 1.7.1
cuda 11.0This project is under the MIT license, and it is based on BasicSR which is under the Apache 2.0 license.
If DRCNet helps your research or work, please consider citing DRCNet.
@InProceedings{lidrcnet_2022_ECCV,
author = {Li, Fei and Shen, Lingfeng and Mi, Yang and Li, Zhenbo},
title = {DRCNet: Dynamic Image Restoration Contrastive Network},
booktitle = {European conference on computer vision},
year = {2022},
organization={Springer}
}
If you have any questions, please contact leefly072@cau.edu.cn or lshen30@jh.edu.
A large part of the code is borrowed from HINet, MPRNet, RN and SimAM. Thanks for their wonderful works!