Prevalent Computational Aberration Correction (CAC) methods are typically tailored to specific optical systems, leading to poor generalization and labor-intensive re-training for new lenses. Developing CAC paradigms capable of generalizing across diverse photographic lenses offers a promising solution to these challenges. However, efforts to achieve such cross-lens universality within consumer photography are still in their early stages due to the lack of a comprehensive benchmark that encompasses a sufficiently wide range of optical aberrations. Furthermore, it remains unclear which specific factors influence existing CAC methods and how these factors affect their performance. In this paper, we present comprehensive experiments and evaluations involving 24 image restoration and CAC algorithms, utilizing our newly proposed UniCAC, a large-scale benchmark for photographic cameras constructed via automatic optical design. The Optical Degradation Evaluator (ODE) is introduced as a novel framework to objectively assess the difficulty of CAC tasks, offering credible quantification of optical aberrations and enabling reliable evaluation. Drawing on our comparative analysis, we identify three key factors -- prior utilization, network architecture, and training strategy -- that most significantly influence CAC performance, and further investigate their respective effects. We believe that our benchmark, dataset, and observations contribute foundational insights to related areas and lay the groundwork for future investigations. Benchmarks, codes, and Zemax files will be available at https://github.com/XiaolongQian/UniCAC.

翻译：当前的计算像差校正方法通常针对特定光学系统定制，导致泛化能力差且需为新镜头重复进行耗时的重新训练。开发能够跨多种摄影镜头泛化的CAC范式为这些挑战提供了有前景的解决方案。然而，由于缺乏涵盖足够广泛光学像差的综合基准，在消费摄影领域实现此类跨镜头通用性的研究仍处于早期阶段。此外，现有CAC方法受哪些具体因素影响以及这些因素如何影响其性能尚不明确。本文通过我们新提出的UniCAC——一个基于自动光学设计构建的大规模摄影相机基准——对24种图像复原与CAC算法进行了综合实验与评估。我们引入了光学退化评估器作为客观评估CAC任务难度的新型框架，提供可信的光学像差量化并实现可靠评估。基于比较分析，我们识别出对CAC性能影响最显著的三个关键因素——先验利用、网络架构和训练策略，并进一步探究了它们各自的影响。我们相信本研究的基准、数据集和观察结果为相关领域提供了基础性见解，并为未来研究奠定了基础。基准数据、代码和Zemax文件将在https://github.com/XiaolongQian/UniCAC发布。