LENVIZ: A High-Resolution Low-Exposure Night Vision Benchmark Dataset
This provides a new benchmark for researchers in low-light image enhancement, though it is incremental as it focuses on dataset creation rather than a novel method.
The authors tackled the challenge of low-light image enhancement by introducing the LENVIZ dataset, which includes over 230K frames with 24K real-world scenes and high-quality ground truth, making it the largest publicly available benchmark up to 4K resolution in the field.
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.