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20212025
most citedEarly Stopping for Deep Image Prior

44 citations · 87 across the 10 of their papers we have counts for

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6 papers · 1 filter

eess.IV2025

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…

eess.IV2023

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…

eess.IV2023

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…

eess.IV2022★ 26 cited

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…

eess.IV2021★ 6 cited

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…

eess.IV2021★ 1 cited

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…