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

Manjushree Aithal, Rosaura G VidalMata, Manikandtan Kartha, Gong Chen, Eashan Adhikarla, Lucas Nedel Kirsten, Zhicheng Fu, Nikhil Ambha Madhusudhana, Joseph V. Nasti; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2026, pp. 2531-2540

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 multiexposure 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.