LENVIZ: A High-Resolution Low-Exposure Night Vision Benchmark Dataset
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Abstract (arXiv)
Low-light image enhancement is crucial for a myriad of applications, from night vision and surveillance, to autonomous driving. However, due to the inherent limitations that come in hand with capturing images in low-illumination environments, the task of enhancing such scenes still presents a formidable challenge. To advance research in this field, we introduce our Low Exposure Night Vision (LENVIZ) Dataset, a comprehensive multi-exposure benchmark dataset for low-light image enhancement comprising of over 230K frames showcasing 24K real-world indoor and outdoor, with-and without human, scenes. Captured using 3 different camera sensors, LENVIZ offers a wide range of lighting conditions, noise levels, and scene complexities, making it the largest publicly available up-to 4K resolution benchmark in the field. LENVIZ includes high quality human-generated ground truth, for which each multi-exposure low-light scene has been meticulously curated and edited by expert photographers to ensure optimal image quality. Furthermore, we also conduct a comprehensive analysis of current state-of-the-art low-light image enhancement techniques on our dataset and highlight potential areas of improvement.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 30 of its 70 references that are in the map. First posted on arXiv 25 Mar 2025.
Builds on
30 of 70 references.
References · 30
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 2018Deep Retinex Decomposition for Low-Light Enhancementcited by 723
- 2020Zero-Reference Deep Curve Estimation for Low-Light Image Enhancementcited by 542
- 2019EnlightenGAN: Deep Light Enhancement without Paired Supervisioncited by 466
- 2018Learning to See in the Darkcited by 460
- 2020Learning Enriched Features for Real Image Restoration and Enhancementcited by 312
- 2020Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancementcited by 303
- 2021Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimationcited by 192
- 2022Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Methodcited by 174
- 2018Getting to Know Low-light Images with The Exclusively Dark Datasetcited by 157
- 2020A Physics-based Noise Formation Model for Extreme Low-light Raw Denoisingcited by 138
- 2021R2RNet: Low-light Image Enhancement via Real-low to Real-normal Networkcited by 106
18 more
- 2022LEDNet: Joint Low-light Enhancement and Deblurring in the Darkcited by 96
- 2022You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correctioncited by 81
- 2022Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppressioncited by 73
- 2021Adaptive Unfolding Total Variation Network for Low-Light Image Enhancementcited by 55
- 2024Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approachcited by 48
- 2017MSR-net:Low-light Image Enhancement Using Deep Convolutional Networkcited by 43
- 2020Towards Fast and Light-Weight Restoration of Dark Imagescited by 24
- 2024You Only Need One Color Space: An Efficient Network for Low-light Image Enhancementcited by 24
- 2023Towards General Low-Light Raw Noise Synthesis and Modelingcited by 19
- 2019Color-wise Attention Network for Low-light Image Enhancementcited by 9
- 2023Aleth-NeRF: Low-light Condition View Synthesis with Concealing Fieldscited by 9
- 2023Few-Shot Domain Adaptation for Low Light RAW Image Enhancementcited by 8
- 2020DALE : Dark Region-Aware Low-light Image Enhancementcited by 5
- 2017Deep Convolutional Denoising of Low-Light Imagescited by 3
- 2024Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure Image Enhancementcited by 3
- 2023Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shiftscited by 2
- 2023A Dual Domain Multi-exposure Image Fusion Network based on the Spatial-Frequency Integrationcited by 2
- 2024MobileMEF: Fast and Efficient Method for Multi-Exposure Fusioncited by 1
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Citing papers per year: 2025: 1.
1 paper.