Zero-3DCE: A Low-Light Video Enhancement for More Robust Computer Vision Tasks

Low-light video enhancement remains a challenge, specifically due to the challenging task of acquiring paired low-light video data. This paper proposes Zero-3DCE, a 3D version of Zero-DCE. Zero-3DCE differs from Zero-DCE by (i) introducing 3D separable convolutions for temporal consistency, (ii) integrating spatial attention for region-specific enhancement, and (iii) combining MS-SSIM and edge-based losses for structural preservation. Separable convolutions are utilized to capture 3D data while maintaining real-time speed, while a spatial attention network guides the model to regions that require enhancement by adaptively weighting spatial regions across all channels. Coupled with YOLOv11m, Zero-3DCE improves detection accuracy under low-light conditions. The model is trained with a combination of single-frame and multi-frame data. Results showed that Zero-3DCE outperformed other low-light enhancers in enhancing both 2D and 3D data while achieving real-time speeds. Zero-3DCE outperforms Zero-DCE by +3.4 dB in PSNR, and achieves up to 0.11 higher SSIM, demonstrating significant perceptual and structural enhancement.