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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 176–200 of 316 papers

TitleStatusHype
Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors—0
UICE-MIRNet guided image enhancement for underwater object detection—0
Low-Light Enhancement Effect on Classification and Detection: An Empirical Study—0
Rethinking the Atmospheric Scattering-driven Attention via Channel and Gamma Correction Priors for Low-Light Image EnhancementCode0
RCNet: Deep Recurrent Collaborative Network for Multi-View Low-Light Image EnhancementCode0
LMT-GP: Combined Latent Mean-Teacher and Gaussian Process for Semi-supervised Low-light Image EnhancementCode0
SDI-Net: Toward Sufficient Dual-View Interaction for Low-light Stereo Image Enhancement—0
Latent Disentanglement for Low Light Image Enhancement—0
ALEN: A Dual-Approach for Uniform and Non-Uniform Low-Light Image EnhancementCode0
RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models—0
Dual High-Order Total Variation Model for Underwater Image RestorationCode0
AGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image Enhancement—0
Low-Light Image Enhancement Based on Cell Vibration Energy Model and Lightness DifferenceCode0
CAPformer: Compression-Aware Pre-trained Transformer for Low-Light Image Enhancement—0
ECAFormer: Low-light Image Enhancement using Cross AttentionCode0
RSEND: Retinex-based Squeeze and Excitation Network with Dark Region Detection for Efficient Low Light Image Enhancement—0
Bilateral Interaction for Local-Global Collaborative Perception in Low-Light Image EnhancementCode0
MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space—0
Unsupervised Image Prior via Prompt Learning and CLIP Semantic Guidance for Low-Light Image Enhancement—0
Text in the Dark: Extremely Low-Light Text Image EnhancementCode0
Seeing Text in the Dark: Algorithm and Benchmark—0
CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement—0
DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement—0
Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach—0
Zero-LED: Zero-Reference Lighting Estimation Diffusion Model for Low-Light Image Enhancement—0
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