Low-Light Image Enhancement
118 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
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).
PPformer: Using pixel-wise and patch-wise cross-attention for low-light image enhancement
DeniJsonC/PPformer
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Computer Vision and Image Understanding 2024
Transformer-based methods perform well in modeling long-range pixel dependencies, which are essential for low-light image enhancement to achieve better lighting, natural colors, and higher contrast.
Low-light Image Enhancement via CLIP-Fourier Guided Wavelet Diffusion
hejh8/cfwd
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Moreover, to further promote the effective recovery of the image details, we combine the Fourier transform based on the wavelet transform and construct a Hybrid High Frequency Perception Module (HFPM) with a significant perception of the detailed features.
A Non-Uniform Low-Light Image Enhancement Method with Multi-Scale Attention Transformer and Luminance Consistency Loss
fang001021/msatr
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Low-light image enhancement aims to improve the perception of images collected in dim environments and provide high-quality data support for image recognition tasks.
Enlighten-Your-Voice: When Multimodal Meets Zero-shot Low-light Image Enhancement
Low-light image enhancement is a crucial visual task, and many unsupervised methods tend to overlook the degradation of visible information in low-light scenes, which adversely affects the fusion of complementary information and hinders the generation of satisfactory results.
Zero-Shot Enhancement of Low-Light Image Based on Retinex Decomposition
Two difficulties here make low-light image enhancement a challenging task; firstly, it needs to consider not only luminance restoration but also image contrast, image denoising and color distortion issues simultaneously.
Global Structure-Aware Diffusion Process for Low-Light Image Enhancement
To harness the capabilities of diffusion models, we delve into this intricate process and advocate for the regularization of its inherent ODE-trajectory.
Lookup Table meets Local Laplacian Filter: Pyramid Reconstruction Network for Tone Mapping
fengzhang427/LLF-LUT
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NeurIPS 2023
Furthermore, we utilize local Laplacian filters to refine the edge details in the high-frequency components in an adaptive manner.
Dimma: Semi-supervised Low Light Image Enhancement with Adaptive Dimming
To fill this gap, we propose Dimma, a semi-supervised approach that aligns with any camera by utilizing a small set of image pairs to replicate scenes captured under extreme lighting conditions taken by that specific camera.
