2 citations · 4 across the 18 of their papers we have counts for
10 papers · 1 filter
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…