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

2026-03-12

AI Summary

Existing computational aberration correction methods suffer from limited generalization, struggling to adapt to diverse lenses without repeated retraining. To address this, this work introduces UniCAC—the first large-scale benchmark for universal computational aberration correction—and systematically evaluates 24 image restoration and correction algorithms. We propose an Optical Degradation Evaluator (ODE) to quantify task difficulty and identify three key factors governing performance: prior exploitation, network architecture, and training strategy. Integrating automated optical design, Zemax simulations, and deep learning, the project provides open-source datasets, code, and optical models, establishing a reproducible foundation for universal aberration correction research and offering principled guidance for algorithm design.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Computational Aberration Correction
• cross-lens generalization
• optical aberrations
• benchmark
• universal CAC

Innovation

• Computational Aberration Correction
• Universal Benchmark
• Optical Degradation Evaluator
• Cross-lens Generalization
• Automatic Optical Design