In digital images, the performance of optical aberration is a multivariate
degradation, where the spectral of the scene, the lens imperfections, and the
field of view together contribute to the results. Besides eliminating it at the
hardware level, the post-processing system, which utilizes various prior
information, is significant for correction. However, due to the content
differences among priors, the pipeline that aligns these factors shows limited
efficiency and unoptimized restoration. Here, we propose a prior quantization
model to correct the optical aberrations in image processing systems. To
integrate these messages, we encode various priors into a latent space and
quantify them by the learnable codebooks. After quantization, the prior codes
are fused with the image restoration branch to realize targeted optical
aberration correction. Comprehensive experiments demonstrate the flexibility of
the proposed method and validate its potential to accomplish targeted
restoration for a specific camera. Furthermore, our model promises to analyze
the correlation between the various priors and the optical aberration of
devices, which is helpful for joint soft-hardware design.

本文提出了一种先验量化模型来改正图像处理中的光学畸变，并用可学习编码书将各种先验信息编码成潜在空间，经过量化后，将先验编码与图像恢复分支进行融合，以实现有针对性的光学畸变纠正。综合实验证明了该方法的灵活性和潜力，并验证了其在固定相机上完成目标修复的能力。此外，本模型承诺分析各种先验信息与设备光学畸变之间的相关性，有助于联合软硬件设计。