迈向摄影相机通用计算像差校正：一份全面的基准分析
Towards Universal Computational Aberration Correction in Photographic Cameras: A Comprehensive Benchmark Analysis
该基准为计算像差校正提供了跨24种图像复原算法的系统性评测，并引入光学退化评估器（ODE）量化任务难度，可直接用于评估和设计面向真实镜头的图像复原方法。
原文依据 we present comprehensive experiments and evaluations involving 24 image restoration and CAC algorithms, utilizing our newly proposed UNICAC
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论主流的计算像差校正（CAC）方法通常针对特定光学系统定制，导致泛化能力差，且对新镜头需要耗费大量人力重新训练。开发能够泛化到多种摄影镜头的CAC范式为这些挑战提供了有前景的解决方案。然而，由于缺乏一个涵盖足够广泛光学像差的全面基准，在消费摄影中实现这种跨镜头通用性的努力仍处于早期阶段。此外，目前尚不清楚哪些具体因素会影响现有的CAC方法，以及这些因素如何影响其性能。本文中，我们利用新提出的UNICAC——一个通过自动光学设计构建的大规模摄影相机基准——对24种图像复原和CAC算法进行了全面的实验与评估。我们引入了光学退化评估器（ODE）作为一种新颖框架，以客观评估CAC任务的难度，提供对光学像差的可靠量化并实现可靠的评估。基于我们的比较分析，我们确定了三个最关键的影响CAC性能的因素——先验利用、网络架构和训练策略——并进一步研究了它们各自的作用。我们相信，我们的基准、数据集和观察结果为相关领域提供了基础性见解，并为未来的研究奠定了基础。基准、代码和Zemax文件将公开。
英文原文摘要 1589 字符
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.