  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
https://github.com/albrateanu/LYT-Net
