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Low-Light Image Enhancement

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.

Papers

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Showing 276–300 of 316 papers

TitleStatusHype
RCNet: Deep Recurrent Collaborative Network for Multi-View Low-Light Image EnhancementCode0
BLNet: A Fast Deep Learning Framework for Low-Light Image Enhancement with Noise Removal and Color RestorationCode0
STAR: A Structure and Texture Aware Retinex ModelCode0
Learning to Kindle the StarlightCode0
Restoring Extremely Dark Images in Real TimeCode0
Edge-Computing-Enabled Deep Learning Approach for Low-Light Satellite Image EnhancementCode0
Bilateral Interaction for Local-Global Collaborative Perception in Low-Light Image EnhancementCode0
MixNet: Efficient Global Modeling for Ultra-High-Definition Image RestorationCode0
Enlighten-Your-Voice: When Multimodal Meets Zero-shot Low-light Image EnhancementCode0
Leveraging Content and Context Cues for Low-Light Image EnhancementCode0
Striving for Faster and Better: A One-Layer Architecture with Auto Re-parameterization for Low-Light Image EnhancementCode0
CUR Transformer: A Convolutional Unbiased Regional Transformer for Image DenoisingCode0
EENet: Frequency-Aware and Spatially Multiscale Network for Single Image DehazingCode0
Zero-Shot Enhancement of Low-Light Image Based on Retinex DecompositionCode0
Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-RegulationCode0
LIME-Eval: Rethinking Low-light Image Enhancement Evaluation via Object DetectionCode0
LIME: Low-light Image Enhancement via Illumination Map EstimationCode0
Linear Array Network for Low-light Image EnhancementCode0
SurroundNet: Towards Effective Low-Light Image EnhancementCode0
ERetinex: Event Camera Meets Retinex Theory for Low-Light Image EnhancementCode0
LLNet: A Deep Autoencoder Approach to Natural Low-light Image EnhancementCode0
Test-Time Training with Self-Supervision for Generalization under Distribution ShiftsCode0
LMT-GP: Combined Latent Mean-Teacher and Gaussian Process for Semi-supervised Low-light Image EnhancementCode0
Text in the Dark: Extremely Low-Light Text Image EnhancementCode0
Brightness Perceiving for Recursive Low-Light Image EnhancementCode0
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