LENVIZ: A High-Resolution Low-Exposure Night Vision Benchmark Dataset
Low-light image enhancement is critical for night vision, surveillance, and autonomous driving, yet hindered by the lack of high-quality, large-scale benchmarks. To address this, we introduce LowLight-4K—the first large-scale, multi-exposure, 4K-resolution low-light night-vision benchmark—comprising 230,000 real-world indoor/outdoor frames captured synchronously via a triple-sensor setup, covering diverse illumination conditions, noise patterns, and semantic complexity. Ground-truth images are meticulously retouched by professional photographers to ensure photorealistic fidelity. LowLight-4K uniquely combines large-scale multi-exposure acquisition with human-refined ground truth and provides a comprehensive, standardized evaluation framework for state-of-the-art methods. Extensive experiments reveal systematic limitations across prevailing approaches in dynamic range recovery, texture preservation, and the noise-structure trade-off. As the largest publicly available 4K low-light enhancement benchmark, LowLight-4K establishes a robust foundation for algorithm development and rigorous performance assessment.