44 citations · 87 across the 10 of their papers we have counts for
10 papers
A Baseline Method for Removing Invisible Image Watermarks using Deep Image Prior
Hengyue Liang, Taihui Li, Ju Sun
Image watermarks have been considered a promising technique to help detect AI-generated content, which can be used to protect copyright or prevent fake image abuse. In this work, w…
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…
Joint Demosaicing and Denoising with Double Deep Image Priors
Taihui Li, Anish Lahiri, Yutong Dai +1
Demosaicing and denoising of RAW images are crucial steps in the processing pipeline of modern digital cameras. As only a third of the color information required to produce a digit…
Predicting the Future of the CMS Detector: Crystal Radiation Damage and Machine Learning at the LHC
Bhargav Joshi, Taihui Li, Buyun Liang +2
The 75,848 lead tungstate crystals in CMS experiment at the CERN Large Hadron Collider are used to measure the energy of electrons and photons produced in the proton-proton collisi…
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…
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…
A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals
Ju Sun, Le Peng, Taihui Li +14
Importance: An artificial intelligence (AI)-based model to predict COVID-19 likelihood from chest x-ray (CXR) findings can serve as an important adjunct to accelerate immediate cli…
Rethinking Transfer Learning for Medical Image Classification
Le Peng, Hengyue Liang, Gaoxiang Luo +2
Transfer learning (TL) from pretrained deep models is a standard practice in modern medical image classification (MIC). However, what levels of features to be reused are problem-de…