Emerging deep-learning-based lens library pre-training (LensLib-PT) pipeline offers a new avenue for blind lens aberration correction by training a universal neural network, demonstrating strong capability in handling diverse unknown optical degradations. This work proposes the OmniLens++ framework, which resolves two challenges that hinder the generalization ability of existing pipelines: the difficulty of scaling data and the absence of prior guidance characterizing optical degradation. To improve data scalability, we expand the design specifications to increase the degradation diversity of the lens source, and we sample a more uniform distribution by quantifying the spatial-variation patterns and severity of optical degradation. In terms of model design, to leverage the Point Spread Functions (PSFs), which intuitively describe optical degradation, as guidance in a blind paradigm, we propose the Latent PSF Representation (LPR). The VQVAE framework is introduced to learn latent features of LensLib's PSFs, which is assisted by modeling the optical degradation process to constrain the learning of degradation priors. Experiments on diverse aberrations of real-world lenses and synthetic LensLib show that OmniLens++ exhibits state-of-the-art generalization capacity in blind aberration correction. Beyond performance, the AODLibpro is verified as a scalable foundation for more effective training across diverse aberrations, and LPR can further tap the potential of large-scale LensLib. The source code and datasets will be made publicly available at https://github.com/zju-jiangqi/OmniLens2.

翻译：新兴的基于深度学习的镜头库预训练（LensLib-PT）流程通过训练通用神经网络，为无镜头像差校正提供了新途径，展现出处理多种未知光学退化的强大能力。本研究提出OmniLens++框架，旨在解决现有流程泛化能力受限的两个关键挑战：数据扩展困难以及缺乏表征光学退化的先验引导。为提升数据可扩展性，我们扩展设计规范以增加镜头源的退化多样性，并通过量化光学退化的空间变化模式与严重程度进行更均匀的分布采样。在模型设计方面，为在无监督范式中利用直观描述光学退化的点扩散函数（PSFs）作为引导，我们提出潜在点扩散函数表示（LPR）。引入VQVAE框架学习镜头库PSFs的潜在特征，并通过建模光学退化过程辅助约束退化先验的学习。在真实世界镜头及合成镜头库的多种像差实验表明，OmniLens++在无像差校正中展现出最先进的泛化能力。除性能外，AODLibpro被验证为可扩展的基础框架，能在多种像差中实现更有效的训练，而LPR能进一步挖掘大规模镜头库的潜力。源代码与数据集将在https://github.com/zju-jiangqi/OmniLens2公开。