18 citations · 32 across the 16 of their papers we have counts for
7 papers · 1 filter
GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction
Tao Hu, Weiyu Zhou, Yanjie Tu +4
Pre-trained Latent Diffusion Models (LDMs) have recently shown strong perceptual priors for low-level vision tasks, making them a promising direction for multi-exposure High Dynami…
NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results
Xiaoning Liu, Zongwei Wu, Florin-Alexandru Vasluianu +102
This paper presents a comprehensive review of the NTIRE 2025 Low-Light Image Enhancement (LLIE) Challenge, highlighting the proposed solutions and final outcomes. The objective of…
Text-Visual Semantic Constrained AI-Generated Image Quality Assessment
Qiang Li, Qingsen Yan, Haojian Huang +3
With the rapid advancements in Artificial Intelligence Generated Image (AGI) technology, the accurate assessment of their quality has become an increasingly vital requirement. Prev…
HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image Enhancement
Qingsen Yan, Kangbiao Shi, Yixu Feng +4
Low-Light Image Enhancement (LLIE) aims to restore vivid content and details from corrupted low-light images. However, existing standard RGB (sRGB) color space-based LLIE methods o…
FusionNet: Multi-model Linear Fusion Framework for Low-light Image Enhancement
Kangbiao Shi, Yixu Feng, Tao Hu +5
The advent of Deep Neural Networks (DNNs) has driven remarkable progress in low-light image enhancement (LLIE), with diverse architectures (e.g., CNNs and Transformers) and color s…
Boosting HDR Image Reconstruction via Semantic Knowledge Transfer
Tao Hu, Longyao Wu, Wei Dong +5
Recovering High Dynamic Range (HDR) images from multiple Standard Dynamic Range (SDR) images become challenging when the SDR images exhibit noticeable degradation and missing conte…
HVI: A New Color Space for Low-light Image Enhancement
Qingsen Yan, Yixu Feng, Cheng Zhang +6
Low-Light Image Enhancement (LLIE) is a crucial computer vision task that aims to restore detailed visual information from corrupted low-light images. Many existing LLIE methods ar…