Deep lens optimization has recently emerged as a new paradigm for designing computational imaging systems, however it has been limited to either simple optical systems consisting of a single DOE or metalens, or the fine-tuning of compound lenses from good initial designs. Here we present a deep lens design method based on curriculum learning, which is able to learn optical designs of compound lenses ab initio from randomly initialized surfaces, therefore overcoming the need for a good initial design. We demonstrate this approach with the fully-automatic design of an extended depth-of-field computational camera in a cellphone-style form factor, highly aspherical surfaces, and a short back focal length.

翻译：深度透镜优化近期已成为设计计算成像系统的新范式，但此前仅局限于由单一衍射光学元件或超透镜构成的简单光学系统，以及基于优质初始设计的复合透镜微调。本文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透镜表面出发，从头学习复合透镜的光学设计，从而克服了对优质初始设计的依赖。我们通过全自动设计一款手机形态因子的扩展景深计算相机（包含高非球面表面和短后焦距）验证了该方法的有效性。