Update 2022.5.11

Our paper has been accepted by IEEE Transactions on Image Processing (TIP）！

CMDSR

Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations.[arxiv][IEEE TIP]

Introduction

CMDSR is a novel conditional meta-network framework which helps the SR framework learn how to adapt to changes in the degradation distribution of input. The ConditionNet of our framework first learns the degradation prior from a support set, which is composed of a series of degraded image patches from the same task. Then the adaptive BaseNet rapidly shifts its parameters according to the conditional features. A task contrastive loss is also proposed to shorten the inner-task distance and enlarge the crosstask distance between task-level features. Without predefining degradation maps, our blind framework can conduct one single parameter update to yield considerable improvement in SR results. More details can be found in our paper

Architecture

Conditional feature extraction at task-level.

Whole Architecture of CMDSR

SR results for real images

Trained model download

You can download our trained model from Google Driver to test with your own LR image.

Acknowledgement

Thanks for the support of Sea-NExT Joint Lab and Bytedance.Inc.

Citation

@article{yin2022conditional,
  title={Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations},
  author={Yin, Guanghao and Wang, Wei and Yuand, Zehuan and Ji, Wei and Yue, Dongdong and Sun, Shouqian and Chua, Tat-Seng and Wang, Changhu},
  journal={IEEE Transactions on Image Processing},
  year={2022},
  publisher={IEEE}
}