222 citations · 555 across the 35 of their papers we have counts for
37 papers
SegDem: Segmentation helps Demosaicing
Ping Chen, Xiangming Wang, Yongyong Chen +5
Image demosaicing reconstructs a full-color image from incomplete color measurements produced by a sensor covered with a color filter array (CFA). Most existing methods formulate d…
Bricker to BRACE: A Bracket Exposure RAW Dataset and Restoration Model for Flicker-Banding
Zihan Zhou, Libo Zhu, Jue Gong +4
Flicker-Banding (FB) arises from temporal aliasing between a camera's rolling shutter and a display's brightness modulation, degrading screen-captured image readability with color…
Deep LoRA-Unfolding Networks for Image Restoration
Xiangming Wang, Haijin Zeng, Benteng Sun +4
Deep unfolding networks (DUNs), combining conventional iterative optimization algorithms and deep neural networks into a multi-stage framework, have achieved remarkable accomplishm…
FideDiff: Efficient Diffusion Model for High-Fidelity Image Motion Deblurring
Xiaoyang Liu, Zhengyan Zhou, Zihang Xu +3
Recent advancements in image motion deblurring, driven by CNNs and transformers, have made significant progress. Large-scale pre-trained diffusion models, which are rich in real-wo…
SlowFast-SCI: Slow-Fast Deep Unfolding Learning for Spectral Compressive Imaging
Haijin Zeng, Xuan Lu, Jiezhang Cao +8
Humans learn in two complementary ways: a slow, cumulative process that builds broad, general knowledge, and a fast, on-the-fly process that captures specific experiences. Existing…
Freqformer: Image-Demoiréing Transformer via Effective Frequency Decomposition
Xiaoyang Liu, Bolin Qiu, Zheng Chen +5
Image demoiréing remains a challenging task due to the complex interplay between texture corruption and color distortions caused by moiré patterns. Existing methods, especially tho…
One-Step Diffusion Model for Image Motion-Deblurring
Xiaoyang Liu, Yuquan Wang, Zheng Chen +4
Currently, methods for single-image deblurring based on CNNs and transformers have demonstrated promising performance. However, these methods often suffer from perceptual limitatio…
One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation
Jianze Li, Jiezhang Cao, Yong Guo +2
Diffusion models (DMs) have significantly advanced the development of real-world image super-resolution (Real-ISR), but the computational cost of multi-step diffusion models limits…
MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging
Zhenghao Pan, Haijin Zeng, Jiezhang Cao +3
Color video snapshot compressive imaging (SCI) employs computational imaging techniques to capture multiple sequential video frames in a single Bayer-patterned measurement. With th…
Unleashing the Power of One-Step Diffusion based Image Super-Resolution via a Large-Scale Diffusion Discriminator
Jianze Li, Jiezhang Cao, Zichen Zou +5
Diffusion models have demonstrated excellent performance for real-world image super-resolution (Real-ISR), albeit at high computational costs. Most existing methods are trying to d…
Mipmap-GS: Let Gaussians Deform with Scale-specific Mipmap for Anti-aliasing Rendering
Jiameng Li, Yue Shi, Jiezhang Cao +4
3D Gaussian Splatting (3DGS) has attracted great attention in novel view synthesis because of its superior rendering efficiency and high fidelity. However, the trained Gaussians su…
DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion Model
Zhenghao Pan, Haijin Zeng, Jiezhang Cao +2
This paper endeavors to advance the precision of snapshot compressive imaging (SCI) reconstruction for multispectral image (MSI). To achieve this, we integrate the advantageous att…
Deep Equilibrium Diffusion Restoration with Parallel Sampling
Jiezhang Cao, Yue Shi, Kai Zhang +3
Diffusion model-based image restoration (IR) aims to use diffusion models to recover high-quality (HQ) images from degraded images, achieving promising performance. Due to the inhe…
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…
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising
Haijin Zeng, Jiezhang Cao, Kai Feng +4
Hyperspectral imaging (HI) has emerged as a powerful tool in diverse fields such as medical diagnosis, industrial inspection, and agriculture, owing to its ability to detect subtle…
Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor
Haijin Zeng, Kai Feng, Jiezhang Cao +5
Pixel binning based Quad sensors have emerged as a promising solution to overcome the hardware limitations of compact cameras in low-light imaging. However, binning results in lowe…
MSFA-Frequency-Aware Transformer for Hyperspectral Images Demosaicing
Haijin Zeng, Kai Feng, Shaoguang Huang +5
Hyperspectral imaging systems that use multispectral filter arrays (MSFA) capture only one spectral component in each pixel. Hyperspectral demosaicing is used to recover the non-me…
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…
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution
Jiezhang Cao, Qin Wang, Yongqin Xian +7
Learning continuous image representations is recently gaining popularity for image super-resolution (SR) because of its ability to reconstruct high-resolution images with arbitrary…
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…
Towards Lightweight Super-Resolution with Dual Regression Learning
Yong Guo, Mingkui Tan, Zeshuai Deng +5
Deep neural networks have exhibited remarkable performance in image super-resolution (SR) tasks by learning a mapping from low-resolution (LR) images to high-resolution (HR) images…
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…