222 citations · 718 across the 18 of their papers we have counts for
21 papers
Low-Light Image Enhancement using Event-Based Illumination Estimation
Lei Sun, Yuhan Bao, Jiajun Zhai +5
Low-light image enhancement (LLIE) aims to improve the visibility of images captured in poorly lit environments. Prevalent event-based solutions primarily utilize events triggered…
Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration
Yawei Li, Bin Ren, Jingyun Liang +5
While vision transformers achieve significant breakthroughs in various image restoration (IR) tasks, it is still challenging to efficiently scale them across multiple types of degr…
Hierarchical Information Flow for Generalized Efficient Image Restoration
Yawei Li, Bin Ren, Jingyun Liang +5
While vision transformers show promise in numerous image restoration (IR) tasks, the challenge remains in efficiently generalizing and scaling up a model for multiple IR tasks. To…
Sharing Key Semantics in Transformer Makes Efficient Image Restoration
Bin Ren, Yawei Li, Jingyun Liang +6
Image Restoration (IR), a classic low-level vision task, has witnessed significant advancements through deep models that effectively model global information. Notably, the emergenc…
Key-Graph Transformer for Image Restoration
Bin Ren, Yawei Li, Jingyun Liang +5
While it is crucial to capture global information for effective image restoration (IR), integrating such cues into transformer-based methods becomes computationally expensive, espe…
MoVideo: Motion-Aware Video Generation with Diffusion Models
Jingyun Liang, Yuchen Fan, Kai Zhang +3
While recent years have witnessed great progress on using diffusion models for video generation, most of them are simple extensions of image generation frameworks, which fail to ex…
Denoising Diffusion Models for Plug-and-Play Image Restoration
Yuanzhi Zhu, Kai Zhang, Jingyun Liang +4
Plug-and-play Image Restoration (IR) has been widely recognized as a flexible and interpretable method for solving various inverse problems by utilizing any off-the-shelf denoiser…
A Unified Framework for Event-based Frame Interpolation with Ad-hoc Deblurring in the Wild
Lei Sun, Daniel Gehrig, Christos Sakaridis +7
Effective video frame interpolation hinges on the adept handling of motion in the input scene. Prior work acknowledges asynchronous event information for this, but often overlooks…
Reference-based Image Super-Resolution with Deformable Attention Transformer
Jiezhang Cao, Jingyun Liang, Kai Zhang +4
Reference-based image super-resolution (RefSR) aims to exploit auxiliary reference (Ref) images to super-resolve low-resolution (LR) images. Recently, RefSR has been attracting gre…
Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising
Jiezhang Cao, Qin Wang, Jingyun Liang +4
Video denoising aims at removing noise from videos to recover clean ones. Some existing works show that optical flow can help the denoising by exploiting the additional spatial-tem…
Towards Interpretable Video Super-Resolution via Alternating Optimization
Jiezhang Cao, Jingyun Liang, Kai Zhang +5
In this paper, we study a practical space-time video super-resolution (STVSR) problem which aims at generating a high-framerate high-resolution sharp video from a low-framerate low…
Recurrent Video Restoration Transformer with Guided Deformable Attention
Jingyun Liang, Yuchen Fan, Xiaoyu Xiang +7
Video restoration aims at restoring multiple high-quality frames from multiple low-quality frames. Existing video restoration methods generally fall into two extreme cases, i.e., t…
Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis
Kai Zhang, Yawei Li, Jingyun Liang +6
While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, su…
VRT: A Video Restoration Transformer
Jingyun Liang, Jiezhang Cao, Yuchen Fan +5
Video restoration (e.g., video super-resolution) aims to restore high-quality frames from low-quality frames. Different from single image restoration, video restoration generally r…
Event-Based Fusion for Motion Deblurring with Cross-modal Attention
Lei Sun, Christos Sakaridis, Jingyun Liang +6
Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times. As a kind of bio-inspired camera, the event camera records the intensity changes in a…
SwinIR: Image Restoration Using Swin Transformer
Jingyun Liang, Jiezhang Cao, Guolei Sun +3
Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). Whil…
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution
Jingyun Liang, Guolei Sun, Kai Zhang +2
Existing blind image super-resolution (SR) methods mostly assume blur kernels are spatially invariant across the whole image. However, such an assumption is rarely applicable for r…
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling
Jingyun Liang, Andreas Lugmayr, Kai Zhang +3
Normalizing flows have recently demonstrated promising results for low-level vision tasks. For image super-resolution (SR), it learns to predict diverse photo-realistic high-resolu…
Boosting Few-shot Semantic Segmentation with Transformers
Guolei Sun, Yun Liu, Jingyun Liang +1
Due to the fact that fully supervised semantic segmentation methods require sufficient fully-labeled data to work well and can not generalize to unseen classes, few-shot segmentati…
Flow-based Kernel Prior with Application to Blind Super-Resolution
Jingyun Liang, Kai Zhang, Shuhang Gu +2
Kernel estimation is generally one of the key problems for blind image super-resolution (SR). Recently, Double-DIP proposes to model the kernel via a network architecture prior, wh…
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
Kai Zhang, Jingyun Liang, Luc Van Gool +1
It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images. Although sev…