activity
20192026
most citedMFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video

268 citations · 516 across the 13 of their papers we have counts for

collaborators
Showing cs.CVShow all

9 papers · 1 filter

cs.CV2026

DuoMatching: Joint-Marginal Distribution Matching for Few-Step Video Generation

Jiahao Zhan, Yan Wang, Yongrui Ma +5

Streaming video generation has benefited from distribution matching distillation (DMD), which matches the joint distribution of video frames to a video teacher's approximation of t…

cs.CV2026

MotionInsight: Diagnosing Object Motion Deficiencies in Generated Videos

Jiahao Zhan, Yongrui Ma, Qunliang Xing +6

Despite rapid progress in video generation models, they still exhibit obvious motion deficiencies, often manifested as incorrect object motion. However, most existing video quality…

cs.CV2026

VQ-Jarvis: Retrieval-Augmented Video Restoration Agent with Sharp Vision and Fast Thought

Xuanyu Zhang, Weiqi Li, Qunliang Xing +6

Video restoration in real-world scenarios is challenged by heterogeneous degradations, where static architectures and fixed inference pipelines often fail to generalize. Recent age…

cs.CV2026

OARS: Process-Aware Online Alignment for Generative Real-World Image Super-Resolution

Shijie Zhao, Xuanyu Zhang, Bin Chen +8

Aligning generative real-world image super-resolution models with human visual preference is challenging due to the perception--fidelity trade-off and diverse, unknown degradations…

cs.CV2024

Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain

Qunliang Xing, Mai Xu, Shengxi Li +4

Existing quality enhancement methods for compressed images focus on aligning the enhancement domain with the raw domain to yield realistic images. However, these methods exhibit a…

cs.CV2022

NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

Ren Yang, Radu Timofte, Meisong Zheng +75

This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the…

cs.CV2022

NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results

Longguang Wang, Yulan Guo, Yingqian Wang +3

In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new sol…

cs.CV2022

Progressive Training of A Two-Stage Framework for Video Restoration

Meisong Zheng, Qunliang Xing, Minglang Qiao +4

As a widely studied task, video restoration aims to enhance the quality of the videos with multiple potential degradations, such as noises, blurs and compression artifacts. Among v…

cs.CV2019★ 268 cited

MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video

Qunliang Xing, Zhenyu Guan, Mai Xu +3

The past few years have witnessed great success in applying deep learning to enhance the quality of compressed image/video. The existing approaches mainly focus on enhancing the qu…