Abstract
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 this https URL.
Abstract (translated)
目前流行的计算图像校正（CAC）方法通常是为了特定的光学系统量身定制，导致泛化能力差，并且在使用新镜头时需要重新训练以耗费大量人力。开发能够跨多种摄影镜头进行泛化的CAC范式为解决这些问题提供了有前景的方法。然而，在消费级摄影中实现这种跨镜头通用性的努力仍处于初期阶段，原因是缺乏涵盖广泛光学像差的全面基准测试。 此外，尚不清楚影响现有CAC方法的具体因素以及这些因素如何影响其性能。在本文中，我们通过使用新提出的UniCAC大规模基准进行综合实验和评估，涉及24种图像恢复和CAC算法，该基准是通过自动光学设计构建的摄影相机专用的。我们引入了光学退化评估器（ODE），这是一种新的框架，用于客观地评估CAC任务的难度，并提供可信的光学像差量化以及可靠的评估方法。 根据我们的比较分析，我们确定了影响CAC性能的三个关键因素：先验利用、网络架构和训练策略，并进一步研究它们各自的影响。我们认为我们的基准测试、数据集和观察结果为相关领域提供了基础见解，并为未来的调查奠定了基础。基准测试、代码和Zemax文件可在以下网址获取（假设URL已提供，实际使用时请插入正确的URL）。
URL
https://arxiv.org/abs/2603.12083
