2 citations · 4 across the 18 of their papers we have counts for
21 papers
Negative Sobolev regularity and optimal decay for the fractional Navier--Stokes--Poisson system
Yuan Xu, Junhao Zhang
We establish global well-posedness and sharp higher-order decay estimates for the fractional compressible Navier--Stokes--Poisson system in with fractional Lamé--Navier di…
The Second LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
Xiang Chen, Hao Li, Jiangxin Dong +89
This paper presents a review of the second LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aims to advance unified image restoration under diverse real-wo…
LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
Xiang Chen, Hao Li, Jiangxin Dong +54
This paper presents a review for the LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aimed to advance research on real-world all-in-one image restoration…
Bridging Fidelity-Reality with Controllable One-Step Diffusion for Image Super-Resolution
Hao Chen, Junyang Chen, Jinshan Pan +1
Recent diffusion-based one-step methods have shown remarkable progress in the field of image super-resolution, yet they remain constrained by three critical limitations: (1) inferi…
FoundIR-v2: Optimizing Pre-Training Data Mixtures for Image Restoration Foundation Model
Xiang Chen, Jinshan Pan, Jiangxin Dong +2
Recent studies have witnessed significant advances in image restoration foundation models driven by improvements in the scale and quality of pre-training data. In this work, we fin…
PGP-DiffSR: Phase-Guided Progressive Pruning for Efficient Diffusion-based Image Super-Resolution
Zhongbao Yang, Jiangxin Dong, Yazhou Yao +2
Although diffusion-based models have achieved impressive results in image super-resolution, they often rely on large-scale backbones such as Stable Diffusion XL (SDXL) and Diffusio…
STCDiT: Spatio-Temporally Consistent Diffusion Transformer for High-Quality Video Super-Resolution
Junyang Chen, Jiangxin Dong, Long Sun +2
We present STCDiT, a video super-resolution framework built upon a pre-trained video diffusion model, aiming to restore structurally faithful and temporally stable videos from degr…
Rethinking Nighttime Image Deraining via Learnable Color Space Transformation
Qiyuan Guan, Xiang Chen, Guiyue Jin +4
Compared to daytime image deraining, nighttime image deraining poses significant challenges due to inherent complexities of nighttime scenarios and the lack of high-quality dataset…
Unbiased Object Detection Beyond Frequency with Visually Prompted Image Synthesis
Xinhao Cai, Liulei Li, Gensheng Pei +4
This paper presents a generation-based debiasing framework for object detection. Prior debiasing methods are often limited by the representation diversity of samples, while naive g…
AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results
George Ciubotariu, Florin-Alexandru Vasluianu, Zhuyun Zhou +44
This paper presents a comprehensive review of the AIM 2025 High FPS Non-Uniform Motion Deblurring Challenge, highlighting the proposed solutions and final results. The objective of…
DeblurDiff: Real-World Image Deblurring with Generative Diffusion Models
Lingshun Kong, Jiawei Zhang, Dongqing Zou +4
Diffusion models have achieved significant progress in image generation. The pre-trained Stable Diffusion (SD) models are helpful for image deblurring by providing clear image prio…
ExpRDiff: Short-exposure Guided Diffusion Model for Realistic Local Motion Deblurring
Zhongbao Yang, Jiangxin Dong, Jinhui Tang +1
Removing blur caused by moving objects is challenging, as the moving objects are usually significantly blurry while the static background remains clear. Existing methods that rely…
FoundIR: Unleashing Million-scale Training Data to Advance Foundation Models for Image Restoration
Hao Li, Xiang Chen, Jiangxin Dong +2
Despite the significant progress made by all-in-one models in universal image restoration, existing methods suffer from a generalization bottleneck in real-world scenarios, as they…
FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution
Junyang Chen, Jinshan Pan, Jiangxin Dong
Faithful image super-resolution (SR) not only needs to recover images that appear realistic, similar to image generation tasks, but also requires that the restored images maintain…
Cascaded Temporal Updating Network for Efficient Video Super-Resolution
Hao Li, Jiangxin Dong, Jinshan Pan
Existing video super-resolution (VSR) methods generally adopt a recurrent propagation network to extract spatio-temporal information from the entire video sequences, exhibiting imp…
Correlation Matching Transformation Transformers for UHD Image Restoration
Cong Wang, Jinshan Pan, Wei Wang +5
This paper proposes UHDformer, a general Transformer for Ultra-High-Definition (UHD) image restoration. UHDformer contains two learning spaces: (a) learning in high-resolution spac…
Ultra-High-Definition Image Restoration: New Benchmarks and A Dual Interaction Prior-Driven Solution
Liyan Wang, Cong Wang, Jinshan Pan +5
Ultra-High-Definition (UHD) image restoration has acquired remarkable attention due to its practical demand. In this paper, we construct UHD snow and rain benchmarks, named UHD-Sno…
ColorMNet: A Memory-based Deep Spatial-Temporal Feature Propagation Network for Video Colorization
Yixin Yang, Jiangxin Dong, Jinhui Tang +1
How to effectively explore spatial-temporal features is important for video colorization. Instead of stacking multiple frames along the temporal dimension or recurrently propagatin…
Collaborative Feedback Discriminative Propagation for Video Super-Resolution
Hao Li, Xiang Chen, Jiangxin Dong +2
The key success of existing video super-resolution (VSR) methods stems mainly from exploring spatial and temporal information, which is usually achieved by a recurrent propagation…
Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining
Xiang Chen, Jinshan Pan, Jiangxin Dong
How to effectively explore multi-scale representations of rain streaks is important for image deraining. In contrast to existing Transformer-based methods that depend mostly on sin…
How Powerful Potential of Attention on Image Restoration?
Cong Wang, Jinshan Pan, Yeying Jin +5
Transformers have demonstrated their effectiveness in image restoration tasks. Existing Transformer architectures typically comprise two essential components: multi-head self-atten…