Towards Universal Computational Aberration Correction in Photographic Cameras: A Comprehensive Benchmark Analysis

• URL: http://arxiv.org/abs/2603.12083v1
• Date: Thu, 12 Mar 2026 15:53:02 GMT
• Title: Towards Universal Computational Aberration Correction in Photographic Cameras: A Comprehensive Benchmark Analysis
• Abstract summary: We present experiments and evaluations involving 24 image restoration and CAC algorithms.<n>We introduce the Optical Degradation Evaluator (ODE) as a novel framework to objectively assess the difficulty of CAC tasks.<n>We identify three key factors -- prior utilization, network architecture, and training strategy -- that most significantly influence CAC performance.
• Score: 51.32617943821609
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/XiaolongQian/UniCAC.

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