Yang Xinge, Fu Qiang, Heidrich Wolfgang
King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Nat Commun. 2024 Aug 3;15(1):6572. doi: 10.1038/s41467-024-50835-7.
Deep optical optimization has recently emerged as a new paradigm for designing computational imaging systems using only the output image as the objective. However, it has been limited to either simple optical systems consisting of a single element such as a diffractive optical element or metalens, or the fine-tuning of compound lenses from good initial designs. Here we present a DeepLens design method based on curriculum learning, which is able to learn optical designs of compound lenses ab initio from randomly initialized surfaces without human intervention, therefore overcoming the need for a good initial design. We demonstrate the effectiveness of our approach by fully automatically designing both classical imaging lenses and a large field-of-view extended depth-of-field computational lens in a cellphone-style form factor, with highly aspheric surfaces and a short back focal length.
深度光学优化最近已成为一种新的范例，用于设计仅以输出图像为目标的计算成像系统。然而，它仅限于由单个元件（如衍射光学元件或超透镜）组成的简单光学系统，或者从良好的初始设计对复合透镜进行微调。在此，我们提出一种基于课程学习的深度透镜设计方法，该方法能够从随机初始化的表面从头开始学习复合透镜的光学设计，无需人工干预，从而克服了对良好初始设计的需求。我们通过以手机样式的外形尺寸全自动设计经典成像镜头和大视场扩展景深计算镜头来证明我们方法的有效性，这些镜头具有高度非球面表面和短后焦距。
