Unsupervised Spectral Demosaicing with Lightweight Spectral Attention
  Networks
Kai Feng Yongqiang Zhao Seong G. Kong Haijin Zeng
This paper presents a deep learning-based spectral demosaicing technique
trained in an unsupervised manner. Many existing deep learning-based techniques
relying on supervised learning with synthetic images, often underperform on
real-world images especially when the number of spectral bands increases.
According to the characteristics of the spectral mosaic image, this paper
proposes a mosaic loss function, the corresponding model structure, a
transformation strategy, and an early stopping strategy, which form a complete
unsupervised spectral demosaicing framework. A challenge in real-world spectral
demosaicing is inconsistency between the model parameters and the computational
resources of the imager. We reduce the complexity and parameters of the
spectral attention module by dividing the spectral attention tensor into
spectral attention matrices in the spatial dimension and spectral attention
vector in the channel dimension, which is more suitable for unsupervised
framework. This paper also presents Mosaic25, a real 25-band hyperspectral
mosaic image dataset of various objects, illuminations, and materials for
benchmarking. Extensive experiments on synthetic and real-world datasets
demonstrate that the proposed method outperforms conventional unsupervised
methods in terms of spatial distortion suppression, spectral fidelity,
robustness, and computational cost.