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.

翻译：波段选择对光谱重建质量有重要影响。为解决这一不适定逆问题，多数波段选择方法采用手工先验，或利用聚类、稀疏正则化约束来寻找最具代表性的波段。这些方法因需要针对不同选择频率或波段组合重复训练，导致计算成本高昂、速度缓慢。许多传统方法依赖场景先验，因此无法适用于其他场景。本文提出一种新型的一次性神经波段选择（NBS）框架用于光谱重建。与采用离散搜索空间和非可微搜索策略的传统方法不同，我们的NBS基于波段选择过程的连续松弛化处理，从而能够利用梯度下降高效搜索波段。为实现在单次过程中兼容任意数量波段的选择，我们进一步利用波段间相关矩阵逐步抑制相似相邻波段。在NTIRE 2022光谱重建挑战赛上的广泛评估表明，当结合四种不同光谱重建方法进行测试时，我们的NBS相较于竞争基线方法取得了一致的性能提升。我们的代码将公开发布。