  As the popularity of mobile photography continues to grow, considerable
effort is being invested in the reconstruction of degraded images. Due to the
spatial variation in optical aberrations, which cannot be avoided during the
lens design process, recent commercial cameras have shifted some of these
correction tasks from optical design to postprocessing systems. However,
without engaging with the optical parameters, these systems only achieve
limited correction for aberrations.In this work, we propose a practical method
for recovering the degradation caused by optical aberrations. Specifically, we
establish an imaging simulation system based on our proposed optical point
spread function model. Given the optical parameters of the camera, it generates
the imaging results of these specific devices. To perform the restoration, we
design a spatial-adaptive network model on synthetic data pairs generated by
the imaging simulation system, eliminating the overhead of capturing training
data by a large amount of shooting and registration. Moreover, we
comprehensively evaluate the proposed method in simulations and experimentally
with a customized digital-single-lens-reflex (DSLR) camera lens and HUAWEI
HONOR 20, respectively. The experiments demonstrate that our solution
successfully removes spatially variant blur and color dispersion. When compared
with the state-of-the-art deblur methods, the proposed approach achieves better
results with a lower computational overhead. Moreover, the reconstruction
technique does not introduce artificial texture and is convenient to transfer
to current commercial cameras. Project Page:
\url{https://github.com/TanGeeGo/ImagingSimulation}.
