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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 126–150 of 316 papers

TitleStatusHype
Illumination-Aware Image Quality Assessment for Enhanced Low-light ImageCode1
Low-light Image Enhancement via Breaking Down the DarknessCode1
Illuminating Darkness: Enhancing Real-world Low-light Scenes with Smartphone ImagesCode1
IceNet for Interactive Contrast EnhancementCode1
HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image EnhancementCode1
LLVIP: A Visible-infrared Paired Dataset for Low-light VisionCode1
Image Demoireing with Learnable Bandpass FiltersCode1
Degrade is Upgrade: Learning Degradation for Low-light Image EnhancementCode1
Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image EnhancementCode1
Troublemaker Learning for Low-Light Image EnhancementCode1
Abandoning the Bayer-Filter to See in the DarkCode1
HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement—0
Deep Joint Unrolling for Deblurring and Low-Light Image Enhancement (JUDE)—0
A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement—0
Low-light Enhancement Method Based on Attention Map Net—0
Gradient-Based Low-Light Image Enhancement—0
GM-MoE: Low-Light Enhancement with Gated-Mechanism Mixture-of-Experts—0
Deep Bilateral Retinex for Low-Light Image Enhancement—0
A Lightweight Low-Light Image Enhancement Network via Channel Prior and Gamma Correction—0
Low-light Image Enhancement Algorithm Based on Retinex and Generative Adversarial Network—0
Decomposition Ascribed Synergistic Learning for Unified Image Restoration—0
FusionNet: Multi-model Linear Fusion Framework for Low-light Image Enhancement—0
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement—0
DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement—0
Low-Light Image Enhancement by Learning Contrastive Representations in Spatial and Frequency Domains—0
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