  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.
