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}.

翻译：随着移动摄影的普及，人们投入大量精力研究退化图像的重建问题。由于光学像差在镜头设计过程中无法避免且具有空间变化特性，近年来的商用相机已将这些校正任务中的一部分从光学设计转向后处理系统。然而，这些系统因未涉及光学参数，仅能实现有限的像差校正效果。本文提出一种实用的光学像差退化恢复方法：具体而言，我们基于所提出的光学点扩散函数模型构建成像模拟系统，通过输入相机光学参数生成特定设备的成像结果。为完成图像复原，我们利用成像模拟系统生成的合成数据对设计空间自适应网络模型，从而省去大量拍摄与配准训练数据的采集成本。此外，我们在仿真实验与定制化单反相机镜头及华为荣耀20的实际测试中全面评估了所提方法。实验表明，该方案能有效消除空间变化模糊与色散现象。相较于当前最先进的去模糊算法，本方法在取得更优结果的同时降低了计算开销。重建技术不会引入人工纹理伪影，且易于迁移至现有商用相机。项目主页：\url{https://github.com/TanGeeGo/ImagingSimulation}。