RNVE: A Real Nighttime Vision Enhancement Benchmark and Dual-Stream Fusion Network

Images captured under challenging low-light conditions often suffer from myriad issues, including diminished contrast and obscured details, stemming from factors such as constrained lighting conditions or pervasive noise interference. Existing learning-based methods struggle in extreme low-light scenarios due to a lack of diverse paired datasets. In this letter, we meticulously curate a challenging real nighttime vision enhancement dataset called RNVE. RNVE comprises diverse data from various devices, including cameras and smartphones, available in both RGB and RAW formats. To enhance data diversity and enable comprehensive algorithm validation, we integrate synthetically generated low-light data, showcasing a spectrum of low-light effects. Additionally, we propose a low-light vision enhancement pipeline based on a dual-stream fusion network, proficiently improving the reconstruction quality of real nighttime scenes and restoring their authentic colors and contrast. Numerous experiments consistently demonstrate that the proposed pipeline excels in low-light enhancement and exhibits robust generalization capabilities across different datasets.