Low-Light Image Enhancement
115 papers with code • 21 benchmarks • 21 datasets
Low-Light Image Enhancement is a computer vision task that involves improving the quality of images captured under low-light conditions. The goal of low-light image enhancement is to make images brighter, clearer, and more visually appealing, without introducing too much noise or distortion.
Benchmarks
These leaderboards are used to track progress in Low-Light Image Enhancement
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Libraries
Use these libraries to find Low-Light Image Enhancement models and implementations
LOL
3DMatch
AFLW
SID
DICM
LLVIP
ExDark
MIT-Adobe FiveK
SMID
Latest papers
NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results.
Equipping Diffusion Models with Differentiable Spatial Entropy for Low-Light Image Enhancement
shermanlian/spatial-entropy-loss
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In this work, we propose a novel method that shifts the focus from a deterministic pixel-by-pixel comparison to a statistical perspective, emphasizing the learning of distributions rather than individual pixel values.
Low-Light Image Enhancement Framework for Improved Object Detection in Fisheye Lens Datasets
daitranskku/aic2024-track4-team15
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This study addresses the evolving challenges in urban traffic monitoring detection systems based on fisheye lens cameras by proposing a framework that improves the efficacy and accuracy of these systems.
AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation
c-yn/adair
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Our approach is motivated by the observation that different degradation types impact the image content on different frequency subbands, thereby requiring different treatments for each restoration task.
You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement
Further, we design a novel Color and Intensity Decoupling Network (CIDNet) with two branches dedicated to processing the decoupled image brightness and color in the HVI space.
Troublemaker Learning for Low-Light Image Enhancement
Second, the predicting model (PM) enhances the brightness of pseudo low-light images.
InstructIR: High-Quality Image Restoration Following Human Instructions
All-In-One image restoration models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model.
LYT-Net: Lightweight YUV Transformer-based Network for Low-Light Image Enhancement
In recent years, deep learning-based solutions have proven successful in the domains of image enhancement.
Edge-Computing-Enabled Deep Learning Approach for Low-Light Satellite Image Enhancement
Edge computing enables rapid data processing and decision-making on satellite payloads.
MixNet: Towards Effective and Efficient UHD Low-Light Image Enhancement
zzr-idam/mixnet
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To capture the long-range dependency of features without introducing excessive computational complexity, we present the Global Feature Modulation Layer (GFML).
