# LENVIZ: A High-Resolution Low-Exposure Night Vision Benchmark Dataset

arXiv: https://arxiv.org/abs/2503.19804 · HTML: https://mlsanity.com/paper/2503.19804/

## Key facts

- Cited by 1 papers 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 (most cited first, 30 of 30)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [Learning to See in the Dark](https://mlsanity.com/paper/1805.01934/) (2018)
- [Learning Enriched Features for Real Image Restoration and Enhancement](https://mlsanity.com/paper/2003.06792/) (2020)
- [Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement](https://mlsanity.com/paper/2012.05609/) (2020)
- [Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation](https://mlsanity.com/paper/2103.00860/) (2021)
- [Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method](https://mlsanity.com/paper/2212.11548/) (2022)
- [Getting to Know Low-light Images with The Exclusively Dark Dataset](https://mlsanity.com/paper/1805.11227/) (2018)
- [A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising](https://mlsanity.com/paper/2003.12751/) (2020)
- [R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network](https://mlsanity.com/paper/2106.14501/) (2021)
- [LEDNet: Joint Low-light Enhancement and Deblurring in the Dark](https://mlsanity.com/paper/2202.03373/) (2022)
- [You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction](https://mlsanity.com/paper/2205.14871/) (2022)
- [Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression](https://mlsanity.com/paper/2207.10564/) (2022)
- [Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2110.00984/) (2021)
- [Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach](https://mlsanity.com/paper/2404.00834/) (2024)
- [MSR-net:Low-light Image Enhancement Using Deep Convolutional Network](https://mlsanity.com/paper/1711.02488/) (2017)
- [Towards Fast and Light-Weight Restoration of Dark Images](https://mlsanity.com/paper/2011.14133/) (2020)
- [You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement](https://mlsanity.com/paper/2402.05809/) (2024)
- [Towards General Low-Light Raw Noise Synthesis and Modeling](https://mlsanity.com/paper/2307.16508/) (2023)
- [Color-wise Attention Network for Low-light Image Enhancement](https://mlsanity.com/paper/1911.08681/) (2019)
- [Aleth-NeRF: Low-light Condition View Synthesis with Concealing Fields](https://mlsanity.com/paper/2303.05807/) (2023)
- [Few-Shot Domain Adaptation for Low Light RAW Image Enhancement](https://mlsanity.com/paper/2303.15528/) (2023)
- [DALE : Dark Region-Aware Low-light Image Enhancement](https://mlsanity.com/paper/2008.12493/) (2020)
- [Deep Convolutional Denoising of Low-Light Images](https://mlsanity.com/paper/1701.01687/) (2017)
- [Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure Image Enhancement](https://mlsanity.com/paper/2407.13170/) (2024)
- [Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shifts](https://mlsanity.com/paper/2312.01540/) (2023)
- [A Dual Domain Multi-exposure Image Fusion Network based on the Spatial-Frequency Integration](https://mlsanity.com/paper/2312.10604/) (2023)
- [MobileMEF: Fast and Efficient Method for Multi-Exposure Fusion](https://mlsanity.com/paper/2408.07932/) (2024)

## Built on by (most cited first, 1 of 1)

- [Diffusion Models for Low-Light Image Enhancement: A Multi-Perspective Taxonomy and Performance Analysis](https://mlsanity.com/paper/2510.05976/) (2025)

## 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.