Towards Blind Lens Aberration Correction via Large LensLib Pre-training and Discrete Degradation Priors
arXiv:2511.17126v4 Announce Type: replace-cross Abstract: Emerging deep-learning-based lens library pre-training (LensLib-PT) pipeline offers a new avenue for blind lens aberration correction by training a universal neural network, demonstrating strong capability in handling diverse unknown optical degradations. This work proposes FoundCAC, a universal foundational framework that resolves two challenges hindering the generalization of existing pipelines: the difficulty of scaling training data and the absence of prior guidance characterizing optical degradation. To improve data scalability, we
Advances in deep learning and computational power are enabling the development of more complex and generalized AI models for specific scientific and engineering challenges.
This development can significantly enhance imaging technologies by autonomously correcting optical flaws, reducing reliance on manual calibration and specialized hardware, thereby improving efficiency and accessibility in various applications.
The ability to 'blindly' correct lens aberrations with a universal foundational framework means imaging systems can become more robust and adaptable to diverse, unknown optical degradations without prior knowledge.
- · Imaging industries
- · Deep learning researchers
- · Computer Vision developers
- · Optical systems manufacturers
- · Manual optical calibration services
- · Legacy specialized aberration correction hardware
Improved image quality and consistency across a wide range of optical devices and conditions.
Accelerated development of autonomous vision systems and robotics due to more reliable input data.
Reduced costs and increased innovation in fields dependent on high-fidelity imaging, such as medical diagnostics and remote sensing.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG