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.

翻译：本文提出了一种基于深度学习的无监督光谱去马赛克技术。现有许多基于深度学习的去马赛克方法依赖合成图像的监督学习，在真实图像上表现不佳，尤其当光谱波段数量增加时。针对光谱马赛克图像的特性，本文提出了一种马赛克损失函数、相应的模型结构、变换策略以及早停策略，从而构建了一个完整的无监督光谱去马赛克框架。真实场景中光谱去马赛克的一个挑战在于模型参数与成像器计算资源之间的不匹配。我们通过将光谱注意力张量在空间维度分解为光谱注意力矩阵、在通道维度分解为光谱注意力向量，降低了光谱注意力模块的复杂度与参数数量，使其更适用于无监督框架。本文还提出了Mosaic25数据集——一个包含多种物体、光照条件与材料类别的真实25波段高光谱马赛克图像基准数据集。在合成与真实数据集上的大量实验表明，所提方法在空间畸变抑制、光谱保真度、鲁棒性以及计算成本方面均优于传统的无监督方法。