222 citations · 604 across the 28 of their papers we have counts for
30 papers
Recurrent Self-Supervised Video Denoising with Denser Receptive Field
Zichun Wang, Yulun Zhang, Debing Zhang +1
Self-supervised video denoising has seen decent progress through the use of blind spot networks. However, under their blind spot constraints, previous self-supervised video denoisi…
Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging
Jiamian Wang, Zongliang Wu, Yulun Zhang +3
Existing reconstruction models in snapshot compressive imaging systems (SCI) are trained with a single well-calibrated hardware instance, making their performance vulnerable to har…
Hierarchical Integration Diffusion Model for Realistic Image Deblurring
Zheng Chen, Yulun Zhang, Ding Liu +4
Diffusion models (DMs) have recently been introduced in image deblurring and exhibited promising performance, particularly in terms of details reconstruction. However, the diffusio…
Binarized Spectral Compressive Imaging
Yuanhao Cai, Yuxin Zheng, Jing Lin +3
Existing deep learning models for hyperspectral image (HSI) reconstruction achieve good performance but require powerful hardwares with enormous memory and computational resources.…
Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution
Jiamian Wang, Huan Wang, Yulun Zhang +2
Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. However, prevailing SR models suffer from prohibitive memory footp…
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…
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
Jing Lin, Xiaowan Hu, Yuanhao Cai +5
How to properly model the inter-frame relation within the video sequence is an important but unsolved challenge for video restoration (VR). In this work, we propose an unsupervised…
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging
Yuanhao Cai, Jing Lin, Haoqian Wang +5
In coded aperture snapshot spectral compressive imaging (CASSI) systems, hyperspectral image (HSI) reconstruction methods are employed to recover the spatial-spectral signal from a…
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction
Yuanhao Cai, Jing Lin, Zudi Lin +5
Existing leading methods for spectral reconstruction (SR) focus on designing deeper or wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB image…
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training
Yuanhao Cai, Xiaowan Hu, Haoqian Wang +3
Existing deep learning real denoising methods require a large amount of noisy-clean image pairs for supervision. Nonetheless, capturing a real noisy-clean dataset is an unacceptabl…
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…
Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution
Bin Sun, Yulun Zhang, Songyao Jiang +1
Convolutional neural network (CNN) has achieved great success on image super-resolution (SR). However, most deep CNN-based SR models take massive computations to obtain high perfor…
Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction
Yuanhao Cai, Jing Lin, Xiaowan Hu +5
Many algorithms have been developed to solve the inverse problem of coded aperture snapshot spectral imaging (CASSI), i.e., recovering the 3D hyperspectral images (HSIs) from a 2D…
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging
Xiaowan Hu, Yuanhao Cai, Jing Lin +5
The rapid development of deep learning provides a better solution for the end-to-end reconstruction of hyperspectral image (HSI). However, existing learning-based methods have two…
Revisiting RCAN: Improved Training for Image Super-Resolution
Zudi Lin, Prateek Garg, Atmadeep Banerjee +6
Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight. However, most SR models were optimized with dated training strategies. In this…
Flow-Guided Sparse Transformer for Video Deblurring
Jing Lin, Yuanhao Cai, Xiaowan Hu +7
Exploiting similar and sharper scene patches in spatio-temporal neighborhoods is critical for video deblurring. However, CNN-based methods show limitations in capturing long-range…
Modeling Mask Uncertainty in Hyperspectral Image Reconstruction
Jiamian Wang, Yulun Zhang, Xin Yuan +2
Recently, hyperspectral imaging (HSI) has attracted increasing research attention, especially for the ones based on a coded aperture snapshot spectral imaging (CASSI) system. Exist…
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction
Yuanhao Cai, Jing Lin, Xiaowan Hu +5
Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from a 2D measurement in the coded aperture snapshot spectral imaging (CASSI) system. The HS…
A Simple and Efficient Reconstruction Backbone for Snapshot Compressive Imaging
Jiamian Wang, Yulun Zhang, Xin Yuan +2
The emerging technology of snapshot compressive imaging (SCI) enables capturing high dimensional (HD) data in an efficient way. It is generally implemented by two components: an op…
Rethinking Adam: A Twofold Exponential Moving Average Approach
Yizhou Wang, Yue Kang, Can Qin +4
Adaptive gradient methods, e.g. \textsc{Adam}, have achieved tremendous success in machine learning. Scaling the learning rate element-wisely by a certain form of second moment est…
Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-Resolution
Xiaoyu Xiang, Yapeng Tian, Yulun Zhang +3
In this paper, we address the space-time video super-resolution, which aims at generating a high-resolution (HR) slow-motion video from a low-resolution (LR) and low frame rate (LF…
ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation
Kai Li, Chang Liu, Handong Zhao +2
This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled sample…
Recent Advances on Neural Network Pruning at Initialization
Huan Wang, Can Qin, Yue Bai +2
Neural network pruning typically removes connections or neurons from a pretrained converged model; while a new pruning paradigm, pruning at initialization (PaI), attempts to prune…
GAN Inversion: A Survey
Weihao Xia, Yulun Zhang, Yujiu Yang +3
GAN inversion aims to invert a given image back into the latent space of a pretrained GAN model, for the image to be faithfully reconstructed from the inverted code by the generato…