  Panoramic Annular Lens (PAL) composed of few lenses has great potential in
panoramic surrounding sensing tasks for mobile and wearable devices because of
its tiny size and large Field of View (FoV). However, the image quality of
tiny-volume PAL confines to optical limit due to the lack of lenses for
aberration correction. In this paper, we propose an Annular Computational
Imaging (ACI) framework to break the optical limit of light-weight PAL design.
To facilitate learning-based image restoration, we introduce a wave-based
simulation pipeline for panoramic imaging and tackle the synthetic-to-real gap
through multiple data distributions. The proposed pipeline can be easily
adapted to any PAL with design parameters and is suitable for loose-tolerance
designs. Furthermore, we design the Physics Informed Image Restoration Network
(PI2RNet) considering the physical priors of panoramic imaging and single-pass
physics-informed engine. At the dataset level, we create the DIVPano dataset
and the extensive experiments on it illustrate that our proposed network sets
the new state of the art in the panoramic image restoration under
spatially-variant degradation. In addition, the evaluation of the proposed ACI
on a simple PAL with only 3 spherical lenses reveals the delicate balance
between high-quality panoramic imaging and compact design. To the best of our
knowledge, we are the first to explore Computational Imaging (CI) in PAL. Code
and datasets are publicly available at
https://github.com/zju-jiangqi/ACI-PI2RNet.
