  This paper tackles spectral reflectance recovery (SRR) from RGB images. Since
capturing ground-truth spectral reflectance and camera spectral sensitivity are
challenging and costly, most existing approaches are trained on synthetic
images and utilize the same parameters for all unseen testing images, which are
suboptimal especially when the trained models are tested on real images because
they never exploit the internal information of the testing images. To address
this issue, we adopt a self-supervised meta-auxiliary learning (MAXL) strategy
that fine-tunes the well-trained network parameters with each testing image to
combine external with internal information. To the best of our knowledge, this
is the first work that successfully adapts the MAXL strategy to this problem.
Instead of relying on naive end-to-end training, we also propose a novel
architecture that integrates the physical relationship between the spectral
reflectance and the corresponding RGB images into the network based on our
mathematical analysis. Besides, since the spectral reflectance of a scene is
independent to its illumination while the corresponding RGB images are not, we
recover the spectral reflectance of a scene from its RGB images captured under
multiple illuminations to further reduce the unknown. Qualitative and
quantitative evaluations demonstrate the effectiveness of our proposed network
and of the MAXL. Our code and data are available at
https://github.com/Dong-Huo/SRR-MAXL.
