2 citations · 3 across the 10 of their papers we have counts for
11 papers
ExpoMotion: A Large-Scale Benchmark and A Householder Projection Network for Multi-Exposure Fusion
Yao Liu, Lishen Qu, Shihao Zhou +6
Multi-Exposure Fusion (MEF) effectively extends dynamic range, but practical deployment is hindered by motion-induced ghosting and the scarcity of high-quality dynamic benchmarks.…
Tool-IQA: Augmenting Image Quality Assessment with Simple Tools
Guanyi Qin, Junjie Zhang, Chunming He +4
Vision-Language Models (VLMs) have been increasingly adopted for Image Quality Assessment (IQA). However, current methods typically employ a static one-shot scoring paradigm, despi…
It Takes Two: A Duet of Periodicity and Directionality for Burst Flicker Removal
Lishen Qu, Shihao Zhou, Jie Liang +3
Flicker artifacts, arising from unstable illumination and row-wise exposure inconsistencies, pose a significant challenge in short-exposure photography, severely degrading image qu…
BurstDeflicker: A Benchmark Dataset for Flicker Removal in Dynamic Scenes
Lishen Qu, Zhihao Liu, Shihao Zhou +5
Flicker artifacts in short-exposure images are caused by the interplay between the row-wise exposure mechanism of rolling shutter cameras and the temporal intensity variations of a…
NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge
Jie Liang, Radu Timofte, Qiaosi Yi +7
In this paper, we present a comprehensive overview of the NTIRE 2025 challenge on the 2nd Restore Any Image Model (RAIM) in the Wild. This challenge established a new benchmark for…
VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank
Tianhe Wu, Jian Zou, Jie Liang +2
DeepSeek-R1 has demonstrated remarkable effectiveness in incentivizing reasoning and generalization capabilities of large language models (LLMs) through reinforcement learning. Nev…
SSL: A Self-similarity Loss for Improving Generative Image Super-resolution
Du Chen, Zhengqiang Zhang, Jie Liang +1
Generative adversarial networks (GAN) and generative diffusion models (DM) have been widely used in real-world image super-resolution (Real-ISR) to enhance the image perceptual qua…
NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge
Jie Liang, Radu Timofte, Qiaosi Yi +6
In this paper, we review the NTIRE 2024 challenge on Restore Any Image Model (RAIM) in the Wild. The RAIM challenge constructed a benchmark for image restoration in the wild, inclu…
A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment
Tianhe Wu, Kede Ma, Jie Liang +2
While Multimodal Large Language Models (MLLMs) have experienced significant advancement in visual understanding and reasoning, their potential to serve as powerful, flexible, inter…
Guided Image Restoration via Simultaneous Feature and Image Guided Fusion
Xinyi Liu, Qian Zhao, Jie Liang +3
Guided image restoration (GIR), such as guided depth map super-resolution and pan-sharpening, aims to enhance a target image using guidance information from another image of the sa…
Human Guided Ground-truth Generation for Realistic Image Super-resolution
Du Chen, Jie Liang, Xindong Zhang +3
How to generate the ground-truth (GT) image is a critical issue for training realistic image super-resolution (Real-ISR) models. Existing methods mostly take a set of high-resoluti…