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

TL;DR

This paper introduces a comprehensive benchmark and evaluation framework for universal computational aberration correction in cameras, analyzing 24 algorithms and identifying key factors affecting performance.

Contribution

It presents UniCAC, a large-scale benchmark for diverse optical aberrations, and introduces the Optical Degradation Evaluator for objective assessment, advancing cross-lens CAC research.

Findings

Prior utilization, network architecture, and training strategy significantly influence CAC performance.

The benchmark enables reliable evaluation across diverse optical aberrations.

Insights lay the foundation for future universal CAC development.

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.