In recent years, deep learning-based solutions have proven successful in the
domains of image enhancement. This paper introduces LYT-Net, or Lightweight YUV
Transformer-based Network, as a novel approach for low-light image enhancement.
The proposed architecture, distinct from conventional Retinex-based models,
leverages the YUV color space's natural separation of luminance (Y) and
chrominance (U and V) to simplify the intricate task of disentangling light and
color information in images. By utilizing the strengths of transformers, known
for their capability to capture long-range dependencies, LYT-Net ensures a
comprehensive contextual understanding of the image while maintaining reduced
model complexity. By employing a novel hybrid loss function, our proposed
method achieves state-of-the-art results on low-light image enhancement
datasets, all while being considerably more compact than its counterparts. The
source code and pre-trained models are available at
this https URL

深度学习在图像增强中取得了成功，本文介绍一种新的轻量级 YUV 变换器网络（LYT-Net）用于低光图像增强，该方法利用 YUV 色彩空间的自然分离性简化了图像中光和颜色信息的复杂任务，通过使用变换器捕捉长距离依赖关系，LYT-Net 保证了对图像的全面上下文理解，同时保持模型复杂性的降低，在低光图像增强数据集上，我们的方法通过采用一种新的混合损失函数实现了最新技术水平的结果，而且比同类方法更紧凑。源代码和预训练模型可在此网址获得：https:// 这里是网址