This paper delves into the substantial challenges faced in end-to-end computer vision applications where optical systems are jointly optimized alongside downstream data-driven models, and explores available solutions.
Data-driven methods to assist lens design have recently begun to emerge; in particular, under the form of lens design extrapolation to find starting points (lenses and freeform reflective system). I proposed a trip over the years to better understand why the AI have been applied first to the starting point problems and where we are going in the future. In this talk, we will explore to most recent progress applications of DNN in optical and lens design. We will also show some working example and discuss the future.
Data-driven methods to assist lens design have recently begun to emerge, in particular under the form of lens design extrapolation: using machine learning, the features of successful lens design forms can be extracted, then recombined to create new designs. Here, we discuss the core aspects and next challenges of the LensNet framework, a deep learning-enabled tool that leverages lens design extrapolation as a more powerful alternative to lens design databases when searching for starting points. We also propose to borrow ideas and tools from the practice of machine learning and deep learning, and integrate them into standard lens design optimization. Namely, we recommend using automatic differentiation to power ray tracing engines, along with considering recent and powerful first-order gradient-based optimizers, and using data-driven glass models that are more suited for optimization than traditional variables.
Data-driven approaches to lens design have only recently begun to emerge. One particular way in which machine learning, and more particularly deep learning, was applied to lens design was by smoothly extrapolating from lens design databases to provide high-quality starting points for lens designers. This mechanism is used by the web application LensNet (which will be publicly available shortly) whose goal is to provide high-quality starting points that are tailored to the desired specifications, namely the effective focal length, f-number and half field of view. Here, we evaluate more thoroughly the designs that are inferred by LensNet and its underlying deep neural network. We provide a global quantitative assessment of the viability of the designs as well as a more targeted comparison among specific design families such as Cooke triplets and Double-Gauss lenses between expert-designed lenses and their automatically inferred counterparts.
Most lens design problems involve the time-consuming task of finding a proper starting point, that is, a lens design that approximately fulfills the desired first-order specifications while decently correcting aberrations. In recent work, a fully-connected (FC) deep neural network was trained to learn this task by extrapolating from known lens design databases. Here, we introduce a new dynamic neural-network architecture for the starting point problem which is based on a recurrent neural network (RNN) architecture. As we show, the dynamic network can learn to infer good starting points on many lens design structures at once whereas the previous model was limited to a given sequence of glass elements and air gaps. We also show that a pretrained RNN model can generalize its knowledge over new lens design structures for which we have no reference lens design and obtain a significantly better optical performance than a RNN trained from scratch.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.