Automatic Lens Design based on Differentiable Ray-tracing

Yang, Xiaowen; Fu, Qiang; Heidrich, Wolfgang

doi:10.1364/cosi.2022.cth4c.2

Cited by 2 publications

(3 citation statements)

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“…The DeepLens optimization uses differentiable raytracing [26,31,34] as an optical simulator. Briefly, the core concept of differentiable ray-tracing is to automatically track derivative information while the calculations of a classical ray-tracing simulation.…”

Section: Differentiable Ray Tracingmentioning

confidence: 99%

“…Most recently, there has been an effort to expand the deep lens design paradigm to compound optical systems composed of multiple refractive optical elements [26,8,31,6,29,34]. The core methodology behind these efforts is optical simulation based on differentiable ray-tracing, in which the evolution of image quality can be tracked as a function of design parameters such as lens curvatures or placements of lens elements.…”

Section: Introductionmentioning

confidence: 99%

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Curriculum Learning for ab initio Deep Learned Refractive Optics

Yang¹,

Fu²,

Heidrich³

2023

Preprint

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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 element such as a diffractive optical element (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 without human intervention, therefore overcoming the need for a good initial design. We demonstrate this approach with the fullyautomatic design of an extended depth-of-field computational camera in a cellphone-style form factor, highly aspherical surfaces, and a short back focal length.

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Section: Differentiable Ray Tracingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Curriculum Learning for ab initio Deep Learned Refractive Optics

Yang¹,

Fu²,

Heidrich³

2023

Preprint

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show abstract

“…For analysis and reconstruction using the system described in sections 3 and 4, we developed a flexible optics and light transport simulation, called GradOptics, in which we implemented our imaging system [8]. This framework is based on geometric optics, which automatically simulates effects such as depth of field, and also can incorporate point spread functions / modulation transfer functions in the imaging process.…”

Section: Jinst 17 P08021 5 Simulation and Reconstructionmentioning

confidence: 99%

Novel light field imaging device with enhanced light collection for cold atom clouds

Cheong

Frisch²,

Gasiorowski³

et al. 2022

J. Inst.

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We present a light field imaging system that captures multiple views of an object with a single shot. The system is designed to maximize the total light collection by accepting a larger solid angle of light than a conventional lens with equivalent depth of field. This is achieved by populating a plane of virtual objects using mirrors and fully utilizing the available field of view and depth of field. Simulation results demonstrate that this design is capable of single-shot tomography of objects of size 𝒪(1 mm3), reconstructing the 3-dimensional (3D) distribution and features not accessible from any single view angle in isolation. In particular, for atom clouds used in atom interferometry experiments, the system can reconstruct 3D fringe patterns with size 𝒪(100 μm). We also demonstrate this system with a 3D-printed prototype. The prototype is used to take images of 𝒪(1 mm3) sized objects, and 3D reconstruction algorithms running on a single-shot image successfully reconstruct 𝒪(100 μm) internal features. The prototype also shows that the system can be built with 3D printing technology and hence can be deployed quickly and cost-effectively in experiments with needs for enhanced light collection or 3D reconstruction. Imaging of cold atom clouds in atom interferometry is a key application of this new type of imaging device where enhanced light collection, high depth of field, and 3D tomographic reconstruction can provide new handles to characterize the atom clouds.

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Automatic Lens Design based on Differentiable Ray-tracing

Abstract: We propose a fully differentiable optical design method enabled by curriculum learning. Preliminary results show that our framework is suitable to solve highly non-convex problems like cellphone lens design.

Cited by 2 publications

References 50 publications

Curriculum Learning for ab initio Deep Learned Refractive Optics

Curriculum Learning for ab initio Deep Learned Refractive Optics

Novel light field imaging device with enhanced light collection for cold atom clouds

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