  Single-pixel imaging can collect images at the wavelengths outside the reach
of conventional focal plane array detectors. However, the limited image quality
and lengthy computational times for iterative reconstruction still impede the
practical application of single-pixel imaging. Recently, deep learning has been
introduced into single-pixel imaging, which has attracted a lot of attention
due to its exceptional reconstruction quality, fast reconstruction speed, and
the potential to complete advanced sensing tasks without reconstructing images.
Here, this advance is discussed and some opinions are offered. Firstly, based
on the fundamental principles of single-pixel imaging and deep learning, the
principles and algorithms of single-pixel imaging based on deep learning are
described and analyzed. Subsequently, the implementation technologies of
single-pixel imaging based on deep learning are reviewed. They are divided into
super-resolution single-pixel imaging, single-pixel imaging through scattering
media, photon-level single-pixel imaging, optical encryption based on
single-pixel imaging, color single-pixel imaging, and image-free sensing
according to diverse application fields. Finally, major challenges and
corresponding feasible approaches are discussed, as well as more possible
applications in the future.
