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Panoramic Annular Lens (PAL) composed of few lenses has great potential inpanoramic surrounding sensing tasks for mobile and wearable devices because ofits tiny size and large Field of View (FoV). However, the image quality oftiny-volume PAL confines to optical limit due to the lack of lenses foraberration correction. In this paper, we propose an Annular ComputationalImaging (ACI) framework to break the optical limit of light-weight PAL design.To facilitate learning-based image restoration, we introduce a wave-basedsimulation pipeline for panoramic imaging and tackle the synthetic-to-real gapthrough multiple data distributions. The proposed pipeline can be easilyadapted to any PAL with design parameters and is suitable for loose-tolerancedesigns. Furthermore, we design the Physics Informed Image Restoration Network(PI2RNet) considering the physical priors of panoramic imaging and single-passphysics-informed engine. At the dataset level, we create the DIVPano datasetand the extensive experiments on it illustrate that our proposed network setsthe new state of the art in the panoramic image restoration underspatially-variant degradation. In addition, the evaluation of the proposed ACIon a simple PAL with only 3 spherical lenses reveals the delicate balancebetween high-quality panoramic imaging and compact design. To the best of ourknowledge, we are the first to explore Computational Imaging (CI) in PAL. Codeand datasets are publicly available athttps://github.com/zju-jiangqi/ACI-PI2RNet.

Annular Computational Imaging: Capture Clear Panoramic Images through Simple Lens