Band selection has a great impact on the spectral recovery quality. To solve
this ill-posed inverse problem, most band selection methods adopt hand-crafted
priors or exploit clustering or sparse regularization constraints to find most
prominent bands. These methods are either very slow due to the computational
cost of repeatedly training with respect to different selection frequencies or
different band combinations. Many traditional methods rely on the scene prior
and thus are not applicable to other scenarios. In this paper, we present a
novel one-shot Neural Band Selection (NBS) framework for spectral recovery.
Unlike conventional searching approaches with a discrete search space and a
non-differentiable search strategy, our NBS is based on the continuous
relaxation of the band selection process, thus allowing efficient band search
using gradient descent. To enable the compatibility for se- lecting any number
of bands in one-shot, we further exploit the band-wise correlation matrices to
progressively suppress similar adjacent bands. Extensive evaluations on the
NTIRE 2022 Spectral Reconstruction Challenge demonstrate that our NBS achieves
consistent performance gains over competitive baselines when examined with four
different spectral recov- ery methods. Our code will be publicly available.

本文提出一种新颖的 spectral recovery 方法 Neural Band Selection (NBS)， 使用基于连续松弛的带选择过程，采用梯度下降进行高效的搜素，并利用基于带相关矩阵的逐步抑制方法，使其适用于选择任意数量的带。NTIRE 2022 Spectral Reconstruction Challenge 的广泛评估表明，我们的 NBS 在四种不同的光谱恢复方法中均取得了一致的性能提升。