  Hyperspectral imaging systems that use multispectral filter arrays (MSFA)
capture only one spectral component in each pixel. Hyperspectral demosaicing is
used to recover the non-measured components. While deep learning methods have
shown promise in this area, they still suffer from several challenges,
including limited modeling of non-local dependencies, lack of consideration of
the periodic MSFA pattern that could be linked to periodic artifacts, and
difficulty in recovering high-frequency details. To address these challenges,
this paper proposes a novel de-mosaicing framework, the MSFA-frequency-aware
Transformer network (FDM-Net). FDM-Net integrates a novel MSFA-frequency-aware
multi-head self-attention mechanism (MaFormer) and a filter-based Fourier
zero-padding method to reconstruct high pass components with greater difficulty
and low pass components with relative ease, separately. The advantage of
Maformer is that it can leverage the MSFA information and non-local
dependencies present in the data. Additionally, we introduce a joint spatial
and frequency loss to transfer MSFA information and enhance training on
frequency components that are hard to recover. Our experimental results
demonstrate that FDM-Net outperforms state-of-the-art methods with 6dB PSNR,
and reconstructs high-fidelity details successfully.
