44 citations · 94 across the 7 of their papers we have counts for
7 papers
Temporal-Consistent Video Restoration with Pre-trained Diffusion Models
Hengkang Wang, Yang Liu, Huidong Liu +5
Video restoration (VR) aims to recover high-quality videos from degraded ones. Although recent zero-shot VR methods using pre-trained diffusion models (DMs) show good promise, they…
DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models
Hengkang Wang, Xu Zhang, Taihui Li +3
Pretrained diffusion models (DMs) have recently been popularly used in solving inverse problems (IPs). The existing methods mostly interleave iterative steps in the reverse diffusi…
Robust Autoencoders for Collective Corruption Removal
Taihui Li, Hengkang Wang, Peng Le +2
Robust PCA is a standard tool for learning a linear subspace in the presence of sparse corruption or rare outliers. What about robustly learning manifolds that are more realistic m…
Interpretable Deep Learning Methods for Multiview Learning
Hengkang Wang, Han Lu, Ju Sun +1
Technological advances have enabled the generation of unique and complementary types of data or views (e.g. genomics, proteomics, metabolomics) and opened up a new era in multiview…
Blind Image Deblurring with Unknown Kernel Size and Substantial Noise
Zhong Zhuang, Taihui Li, Hengkang Wang +1
Blind image deblurring (BID) has been extensively studied in computer vision and adjacent fields. Modern methods for BID can be grouped into two categories: single-instance methods…
Early Stopping for Deep Image Prior
Hengkang Wang, Taihui Li, Zhong Zhuang +3
Deep image prior (DIP) and its variants have showed remarkable potential for solving inverse problems in computer vision, without any extra training data. Practical DIP models are…
Self-Validation: Early Stopping for Single-Instance Deep Generative Priors
Taihui Li, Zhong Zhuang, Hengyue Liang +3
Recent works have shown the surprising effectiveness of deep generative models in solving numerous image reconstruction (IR) tasks, even without training data. We call these models…