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

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.

Figures (16)

• Figure 1: Comparison of the linear relationship between RMS radius and ODE with the CAC results. (a) We select $12$ lenses and use the same model chen2022real for lens-specific training. The red line shows the least squares linear fit, with $R^2$ indicating correlation strength (closer to $1$ means stronger correlation). (b) Visual results: Lens1, with a larger RMS radius, retains more details, while Lens2, with a smaller RMS radius, loses fine structures.
• Figure 2: Optical Degradation Evaluator (ODE) pipeline. It assesses lens optical degradation severity via standard checkerboard imaging. The spatial branch calculates OIQ for various FoVs, the channel branch for different channels.
• Figure 3: Benchmark overview. The sampled lenses are divided into $5$ levels based on ODE, with $3$ lenses from each level showcased to facilitate presentation. Each lens is illustrated with the ray tracing diagram, PSFs for $3$ FoVs, and corresponding aberration image patches. The distribution of quantification dimensions for each lens is also presented in a bar chart.
• Figure 4: (a) The ray tracing diagram, spot diagrams and RMS spot sizes produced by the simulation framework closely resemble those obtained from Zemax. The tested system is a single-lens optical system. (b) Comparison between simulation and real-shot aberration images.
• Figure 5: (a) Relevance between ODE components and CAC performance. (b) Distribution of different quantification dimensions in our sampled lens library.