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:
https://github.com/TanGeeGo/ImagingSimulation.

本研究提出了一种用于恢复因光学像差导致的图像退化的实用方法，通过基于我们提出的光学点扩散函数模型的成像模拟系统，生成特定设备的成像结果，并在合成数据对上设计空间自适应网络模型，消除了大量拍摄和注册训练数据的开销。实验结果表明，该方法成功地消除了空间变异模糊和色散，并具有更低的计算开销和便于转移到当前商业相机。

使用成像模拟进行后处理的光学像差校正

Optical Aberration Correction in Postprocessing using Imaging Simulation

Fast and accurate simulation of imaging through atmospheric turbulence is
essential for developing turbulence mitigation algorithms. Recognizing the
limitations of previous approaches, we introduce a new concept known as the
phase-to-space (P2S) transform to significantly speed up the simulation. P2S is
build upon three ideas: (1) reformulating the spatially varying convolution as
a set of invariant convolutions with basis functions, (2) learning the basis
function via the known turbulence statistics models, (3) implementing the P2S
transform via a light-weight network that directly convert the phase
representation to spatial representation. The new simulator offers 300x --
1000x speed up compared to the mainstream split-step simulators while
preserving the essential turbulence statistics.

本文提出了一种基于 phase-to-space transform 的新模拟方法，该模拟方法通过不变卷积和基函数学习，结合轻量化网络直接将相位转化为空间形式，以实现有效的大气湍流成像模拟，相对主流分裂步进模拟器，该模拟器可获得 300 倍至 1000 倍的速度提升，并保持了必要的湍流统计分析。