18 citations · 23 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
Xiaoning Liu, Zongwei Wu, Ao Li +109
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective netw…
You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement
Qingsen Yan, Yixu Feng, Cheng Zhang +5
Low-Light Image Enhancement (LLIE) task tends to restore the details and visual information from corrupted low-light images. Most existing methods learn the mapping function betwee…