Opt Express. 2023 Apr 24;31(9):13585-13600. doi: 10.1364/OE.485258.
Optical aberrations of optical systems cause significant degradation of imaging quality. Aberration correction by sophisticated lens designs and special glass materials generally incurs high cost of manufacturing and the increase in the weight of optical systems, thus recent work has shifted to aberration correction with deep learning-based post-processing. Though real-world optical aberrations vary in degree, existing methods cannot eliminate variable-degree aberrations well, especially for the severe degrees of degradation. Also, previous methods use a single feed-forward neural network and suffer from information loss in the output. To address the issues, we propose a novel aberration correction method with an invertible architecture by leveraging its information-lossless property. Within the architecture, we develop conditional invertible blocks to allow the processing of aberrations with variable degrees. Our method is evaluated on both a synthetic dataset from physics-based imaging simulation and a real captured dataset. Quantitative and qualitative experimental results demonstrate that our method outperforms compared methods in correcting variable-degree optical aberrations.
光学系统的像差会导致成像质量显著下降。通过复杂的镜头设计和特殊的玻璃材料来校正像差通常会增加制造成本和光学系统的重量，因此最近的工作已经转向基于深度学习的后处理来进行像差校正。尽管实际的光学像差程度不同，但现有的方法不能很好地消除可变程度的像差，特别是对于严重的退化程度。此外，以前的方法使用单个前馈神经网络，并且在输出中存在信息丢失。为了解决这些问题，我们提出了一种具有反演架构的新型像差校正方法，利用其信息无损特性。在该架构中，我们开发了条件可逆块，以允许处理具有可变程度的像差。我们的方法在基于物理成像模拟的合成数据集和真实拍摄的数据集上进行了评估。定量和定性的实验结果表明，我们的方法在校正可变程度的光学像差方面优于其他方法。
