One-shot neural band selection for spectral recovery
Hai-Miao Hu Zhenbo Xu Wenshuai Xu You Song YiTao Zhang Liu Liu Zhilin Han Ajin Meng
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.