DEEP LEARNING-BASED VIDEO LIGHT IMAGE ENHANCEMENT FOR IMPROVED VISIBILITY
Keywords:
low light, video enhancement, deep learning, visibility, noise reductionAbstract
Low light conditions present significant challenges in video capture and processing, often resulting in reduced visibility and increased noise. Traditional video enhancement methods typically involve converting video frames to images and applying image processing techniques such as histogram equalization, contrast stretching, and noise reduction filters. While these approaches can provide some
improvement, they often fail to produce visually pleasing or natural results.
References
Guo, Chunle, et al. "Zero-reference deep curve estimation for low-light image enhancement." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.
Yang, Wenhan, et al. "From fidelity to perceptual quality: A semi-supervised approach for low light image enhancement." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.
