mlsanityData to 7 Oct 2026

End-to-End Hybrid Refractive-Diffractive Lens Design with Differentiable Ray-Wave Model

arXiv 2406.00834 ↗First posted 2 Jun 2024cs.GR cs.CV physics.opticscited by 11

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Abstract (arXiv)

Hybrid refractive-diffractive lenses combine the light efficiency of refractive lenses with the information encoding power of diffractive optical elements (DOE), showing great potential as the next generation of imaging systems. However, accurately simulating such hybrid designs is generally difficult, and in particular, there are no existing differentiable image formation models for hybrid lenses with sufficient accuracy. In this work, we propose a new hybrid ray-tracing and wave-propagation (ray-wave) model for accurate simulation of both optical aberrations and diffractive phase modulation, where the DOE is placed between the last refractive surface and the image sensor, i.e. away from the Fourier plane that is often used as a DOE position. The proposed ray-wave model is fully differentiable, enabling gradient back-propagation for end-to-end co-design of refractive-diffractive lens optimization and the image reconstruction network. We validate the accuracy of the proposed model by comparing the simulated point spread functions (PSFs) with theoretical results, as well as simulation experiments that show our model to be more accurate than solutions implemented in commercial software packages like Zemax. We demonstrate the effectiveness of the proposed model through real-world experiments and show significant improvements in both aberration correction and extended depth-of-field (EDoF) imaging. We believe the proposed model will motivate further investigation into a wide range of applications in computational imaging, computational photography, and advanced optical design. Code will be released upon publication.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 13 of its 68 references that are in the map. First posted on arXiv 2 Jun 2024.

Builds on

13 of 68 references.

References · 13

  1. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  2. 2022Simple Baselines for Image Restorationcited by 701
  3. 2019Deep Optics for Monocular Depth Estimation and 3D Object Detectioncited by 65
  4. 2019Deep Optics for Single-shot High-dynamic-range Imagingcited by 62
  5. 2020Single-shot Hyperspectral-Depth Imaging with Learned Diffractive Opticscited by 39
  6. 2023Curriculum Learning for ab initio Deep Learned Refractive Opticscited by 32
  7. 2023Optical Aberration Correction in Postprocessing using Imaging Simulationcited by 18
  8. 2022The Differentiable Lens: Compound Lens Search over Glass Surfaces and Materials for Object Detectioncited by 15
  9. 2023Thin On-Sensor Nanophotonic Array Camerascited by 13
  10. 2021Neural 'Etendue Expander for Ultra-Wide-Angle High-Fidelity Holographic Displaycited by 11
  11. 2023Task-Driven Lens Designcited by 9
  12. 2022Hybrid Diffractive Optics Design via Hardware-in-the-Loop Methodology for Achromatic Extended-Depth-of-Field Imagingcited by 7
1 more
  1. 2021Diffractive lensless imaging with optimized Voronoi-Fresnel phasecited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 3.

11 papers.