Neural Invertible Variable-degree Optical Aberrations Correction
This work addresses the challenge of cost-effective aberration correction for imaging systems, though it appears incremental as it builds on deep learning-based post-processing approaches.
The paper tackles the problem of correcting variable-degree optical aberrations in optical systems, which degrade imaging quality, by proposing a novel invertible neural network architecture that outperforms existing methods on both synthetic and real datasets.
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.