Computer vision aims to enable machines to understand the visual world. Computer vision encompasses numerous tasks, namely action recognition, object detection and image classification. Much research has been focused on solving these tasks, but one that remains relatively uncharted is light enhancement (LE). Low-light enhancement (LLE) is crucial as computer vision tasks fail in the absence of sufficient lighting, having to rely on the addition of peripherals such as sensors. This review paper will shed light on this (focusing on video enhancement) subfield of computer vision, along with the other forementioned computer vision tasks. The review analyzes both traditional and deep learning-based enhancers and provides a comparative analysis on recent models in the field. The review also analyzes how popular computer vision tasks are improved and made more robust when coupled with light enhancement algorithms. Results show that deep learners outperform traditional enhancers, with supervised learners obtaining the best results followed by zero-shot learners, while computer vision tasks are improved with light enhancement coupling. The review concludes by highlighting major findings such as that although supervised learners obtain the best results, due to a lack of real-world data and robustness to new data, a shift to zero-shot learners is required.

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.