Jingyun Liang

I am currently a PhD Student at Computer Vision Lab, ETH Zürich, Switzerland. I am co-supervised by Prof. Luc Van Gool and Prof. Radu Timofte. I also work closely with Dr. Kai Zhang. I mainly focus on low-level vision research, especially on image and video restoration, such as super-resolution, deblurring and denoising.

Email / Google Scholar / Github

News

2022-06-10: Our new paper RVRT achieves SOTA video restoration results with balanced size, memory and runtime.
2022-04-01: Our new paper SCUNet achieves awesome denoising performance for real-world images.
2022-01-28: Our new paper VRT outperforms previous video SR/ deblurring/ denoising/ frame interpolation/ space-time video SR methods by up to 2.16dB!
2021-10-20: SwinIR is awarded the best paper prize in ICCV-AIM2021.
2021-08-01: Three papers (HCFlow, MANet and BSRGAN) accepted by ICCV2021.
2021-03-29: One paper (FKP) accepted by CVPR2021.

Research

	Recurrent Video Restoration Transformer with Guided Deformable Attention Jingyun Liang, Yuchen Fan, Xiaoyu Xiang, Rakesh Ranjan, Eddy Ilg, Simon Green, Jiezhang Cao, Kai Zhang, Radu Timofte, Luc Van Gool arxiv, 2022 arXiv / code / bibtex A transformer-based model that jointly extracts, aligns, and fuses frame features at multiple scales; state-of-the-art performance in video SR/ deblurring/ denoising.
	Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis Kai Zhang, Yawei Li, Jingyun Liang, Jiezhang Cao, Yulun Zhang, Hao Tang, Radu Timofte and Luc Van Gool arxiv, 2022 arXiv / code / bibtex A practical real-world image denoising model with impressive results on real-world images.
	VRT: A Video Restoration Transformer Jingyun Liang, Jiezhang Cao, Yuchen Fan, Kai Zhang, Rakesh Ranjan, Yawei Li, Radu Timofte and Luc Van Gool arxiv, 2022 arXiv / code / bibtex A transformer-based model that jointly extracts, aligns, and fuses frame features at multiple scales; state-of-the-art performance in video SR/ deblurring/ denoising/ frame interpolation/ space-time video SR.
	Event-Based Fusion for Motion Deblurring with Cross-modal Attention Lei Sun, Christos Sakaridis, Jingyun Liang, Qi Jiang, Kailun Yang, Peng Sun, Yaozu Ye, Kaiwei Wang and Luc Van Gool arxiv, 2021 arXiv / code (coming soon) / dataset (coming soon) / bibtex An unfolded end-to-end event camera-based motion deblurring method; a High-Quality Blur (HQBlur) dataset for event camera-based deblurring.
	SwinIR: Image Restoration Using Swin Transformer Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool and Radu Timofte IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 arXiv / code / online demo / bibtex A transformer-based image restoration that allows content-based interactions and long-range dependency modelling; state-of-the-art performance on image SR, denoising and JPEG compression artifact reduction.
	Designing a Practical Degradation Model for Deep Blind Image Super-Resolution Kai Zhang, Jingyun Liang, Luc Van Gool and Radu Timofte IEEE/CVF International Conference on Computer Vision (ICCV), 2021 arXiv / code / online demo / bibtex The first practical real-world degradation model for training real-world image SR models; impressive results on real-world images.
	Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling Jingyun Liang, Andreas Lugmayr, Kai Zhang, Martin Danelljan, Luc Van Gool and Radu Timofte IEEE/CVF International Conference on Computer Vision (ICCV), 2021 arXiv / code / online demo / bibtex It learns a bijective mapping between HR and LR image pairs by modelling low and high-frequency components; a unified framework for image SR and image rescaling.
	Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution Jingyun Liang, Guolei Sun, Kai Zhang, Luc Van Gool and Radu Timofte IEEE/CVF International Conference on Computer Vision (ICCV), 2021 arXiv / code / online demo / bibtex A kernel estimation network with mutual affine convolution for spatially variant kernels; state-of-the-art blind SR performance.
	Flow-based Kernel Prior with Application to Blind Super-Resolution Jingyun Liang, Kai Zhang, Shuhang Gu, Luc Van Gool, Radu Timofte IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 arXiv / code / bibtex A normalizing flow-based kernel prior for kernel modeling; state-of-the-art performance in unsupervised blind SR.

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