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。

一次性神经带选择用于频谱恢复

One-shot neural band selection for spectral recovery

To reconstruct spectral signals from multi-channel observations, in
particular trichromatic RGBs, has recently emerged as a promising alternative
to traditional scanning-based spectral imager. It has been proven that the
reconstruction accuracy relies heavily on the spectral response of the RGB
camera in use. To improve accuracy, data-driven algorithms have been proposed
to retrieve the best response curves of existing RGB cameras, or even to design
brand new three-channel response curves. Instead, this paper explores the
filter-array based color imaging mechanism of existing RGB cameras, and
proposes to design the IR-cut filter properly for improved spectral recovery,
which stands out as an in-between solution with better trade-off between
reconstruction accuracy and implementation complexity. We further propose a
deep learning based spectral reconstruction method, which allows to recover the
illumination spectrum as well. Experiment results with both synthetic and real
images under daylight illumination have shown the benefits of our IR-cut filter
tuning method and our illumination-aware spectral reconstruction method.

本研究提出了一种基于滤光阵列的颜色成像机制，旨在设计 IR 剪切滤光片，以改善现有 RGB 相机的光谱恢复，进而提出一个基于深度学习的光谱重建方法，并得到了合成和真实图像试验结果的验证。