INTRODUCTION
1 DEEP LEARNING-BASED LLIE
1.1 Problem Definition
1.2 Learning Strategies
2 TECHNICAL REVIEW AND DISCUSSION
2.1 Network Structure
2.2 Combination of Deep Model and Retinex Theory
2.3 Data Format
2.4 Loss Function
2.5 Training Datasets
2.6 Testing Datasets
2.7 Evaluation Metrics
3 BENCHMARKING AND EMPIRICAL ANALYSIS
3.1 A New Low-Light Image and Video Dataset
3.2 Online Evaluation Platform
3.3 Benchmarking Results
3.4 Computational Complexity
3.5 Application-Based Evaluation
3.6 Discussion