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Low-Light Image Enhancement
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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.
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A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement
A Poisson-Guided Decomposition Network for Extreme Low-Light Image Enhancement
RT-X Net: RGB-Thermal cross attention network for Low-Light Image Enhancement
URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration
CURVE: CLIP-Utilized Reinforcement Learning for Visual Image Enhancement via Simple Image Processing
See through the Dark: Learning Illumination-affined Representations for Nighttime Occupancy Prediction
Forward-only Diffusion Probabilistic Models
Degradation-Aware Feature Perturbation for All-in-One Image Restoration
Entropy-Driven Genetic Optimization for Deep-Feature-Guided Low-Light Image Enhancement
PDE: Gene Effect Inspired Parameter Dynamic Evolution for Low-light Image Enhancement
A review of advancements in low-light image enhancement using deep learning
HiLLIE: Human-in-the-Loop Training for Low-Light Image Enhancement
DeepSPG: Exploring Deep Semantic Prior Guidance for Low-light Image Enhancement with Multimodal Learning
FusionNet: Multi-model Linear Fusion Framework for Low-light Image Enhancement
Structure-guided Diffusion Transformer for Low-Light Image Enhancement
Towards Scale-Aware Low-Light Enhancement via Structure-Guided Transformer Design
Learning Physics-Informed Color-Aware Transforms for Low-Light Image Enhancement
Towards Realistic Low-Light Image Enhancement via ISP Driven Data Modeling
Low-Light Image Enhancement using Event-Based Illumination Estimation
Nonlocal Retinex-Based Variational Model and its Deep Unfolding Twin for Low-Light Image Enhancement
Brightness Perceiving for Recursive Low-Light Image Enhancement
HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement
LENVIZ: A High-Resolution Low-Exposure Night Vision Benchmark Dataset
Illuminating Darkness: Enhancing Real-world Low-light Scenes with Smartphone Images
GM-MoE: Low-Light Enhancement with Gated-Mechanism Mixture-of-Experts
ERetinex: Event Camera Meets Retinex Theory for Low-Light Image Enhancement
Self-supervision via Controlled Transformation and Unpaired Self-conditioning for Low-light Image Enhancement
Striving for Faster and Better: A One-Layer Architecture with Auto Re-parameterization for Low-Light Image Enhancement
HVI: A New color space for Low-light Image Enhancement
LUMINA-Net: Low-light Upgrade through Multi-stage Illumination and Noise Adaptation Network for Image Enhancement
EENet: Frequency-Aware and Spatially Multiscale Network for Single Image Dehazing
Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration
DLEN: Dual Branch of Transformer for Low-Light Image Enhancement in Dual Domains
FLOL: Fast Baselines for Real-World Low-Light Enhancement
Natural Language Supervision for Low-light Image Enhancement
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