Jingyun Liang JingyunLiang

Jingyun Liang

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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

image/video super-resolution (SR)
image/video deblurring
image/video denoising
...

🚀 News

2022-10-04: Our new paper RVRT, NeurlPS2022 achieves SOTA video restoration results with balanced size, memory and runtime.
2022-08-30: See our papers on real-world image denoising (SCUNet) and video denoising (ReViD).
2022-07-30: Three papers, including EFNet (event-based image deblurring, oral), DATSR (reference image SR) and DAVSR (video SR), accepted by ECCV2022.
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.

🌱 Repositories

Topic	Title	Badge
real-world video denoising	Practical Real Video Denoising with Realistic Degradation Model
event-based image deblurring	Event-based Fusion for Motion Deblurring with Cross-modal Attention, ECCV2022
reference image SR	Reference-based Image Super-Resolution with Deformable Attention Transformer, ECCV2022
interpretable video restoration	Towards Interpretable Video Super-Resolution via Alternating Optimization, ECCV2022
transformer-based video restoration	Recurrent Video Restoration Transformer with Guided Deformable Attention
transformer-based video restoration	VRT: A Video Restoration Transformer
transformer-based image restoration	SwinIR: Image Restoration Using Swin Transformer
real-world image denoising	Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis
real-world image SR	Designing a Practical Degradation Model for Deep Blind Image Super-Resolution, ICCV2021
blind image SR	Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution, ICCV2021
blind image SR	Flow-based Kernel Prior with Application to Blind Super-Resolution, CVPR2021
normalizing flow-based image SR and image rescaling	Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling, ICCV2021
image/ video restoration	Image/ Video Restoration Toolbox

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