Tolerance-Aware Deep Optics

• URL: http://arxiv.org/abs/2502.04719v1
• Date: Fri, 07 Feb 2025 07:42:25 GMT
• Title: Tolerance-Aware Deep Optics
• Authors: Jun Dai, Liqun Chen, Xinge Yang, Yuyao Hu, Jinwei Gu, Tianfan Xue,
• Abstract summary: Deep optics has emerged as a promising approach by co-designing optical elements with deep learning algorithms. We present the first end-to-end tolerance-aware optimization framework that incorporates multiple tolerance types into the deep optics design pipeline. Our method combines physics-informed modelling with data-driven training to enhance optical design by accounting for and compensating for structural deviations in manufacturing and assembly.
• Score: 15.445359232123133
• License:
• Abstract: Deep optics has emerged as a promising approach by co-designing optical elements with deep learning algorithms. However, current research typically overlooks the analysis and optimization of manufacturing and assembly tolerances. This oversight creates a significant performance gap between designed and fabricated optical systems. To address this challenge, we present the first end-to-end tolerance-aware optimization framework that incorporates multiple tolerance types into the deep optics design pipeline. Our method combines physics-informed modelling with data-driven training to enhance optical design by accounting for and compensating for structural deviations in manufacturing and assembly. We validate our approach through computational imaging applications, demonstrating results in both simulations and real-world experiments. We further examine how our proposed solution improves the robustness of optical systems and vision algorithms against tolerances through qualitative and quantitative analyses. Code and additional visual results are available at openimaginglab.github.io/LensTolerance.

Related papers

• Successive optimization of optics and post-processing with differentiable coherent PSF operator and field information [9.527960631238173]
  We introduce a precise optical simulation model, and every operation in pipeline is differentiable. To efficiently address various degradation, we design a joint optimization procedure that leverages field information.
  arXiv  Detail & Related papers  (2024-12-19T07:49:40Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation. deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency. This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• A Differentiable Wave Optics Model for End-to-End Computational Imaging System Optimization [19.83939112821776]
  End-to-end optimization has emerged as a powerful data-driven method for computational imaging system design. It is challenging to model both aberration and diffraction in light transport for end-to-end optimization of compound optics. We propose a differentiable optics simulator that efficiently models both aberration and diffraction for compound optics.
  arXiv  Detail & Related papers  (2024-12-13T00:57:47Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Global Search Optics: Automatically Exploring Optimal Solutions to Compact Computational Imaging Systems [15.976326291076377]
  The popularity of mobile vision creates a demand for advanced compact computational imaging systems. Joint design pipelines come to the forefront, where the two significant components are simultaneously optimized via data-driven learning. In this work, we present Global Search Optimization (GSO) to design compact computational imaging systems.
  arXiv  Detail & Related papers  (2024-04-30T01:59:25Z)
• Neural Lithography: Close the Design-to-Manufacturing Gap in Computational Optics with a 'Real2Sim' Learned Photolithography Simulator [2.033983045970252]
  We introduce neural lithography to address the 'design-to-manufacturing' gap in computational optics. We propose a fully differentiable design framework that integrates a pre-trained photolithography simulator into the model-based optical design loop.
  arXiv  Detail & Related papers  (2023-09-29T15:50:26Z)
• Revealing the preference for correcting separated aberrations in joint optic-image design [19.852225245159598]
  We characterize the optics with separated aberrations to achieve efficient joint design of complex systems such as smartphones and drones. An image simulation system is presented to reproduce the genuine imaging procedure of lenses with large field-of-views. Experiments reveal that the preference for correcting separated aberrations in joint design is as follows: longitudinal chromatic aberration, lateral chromatic aberration, spherical aberration, field curvature, and coma, with astigmatism coming last.
  arXiv  Detail & Related papers  (2023-09-08T14:12:03Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement [58.72667941107544]
  A typical framework is to simultaneously estimate the illumination and reflectance, but they disregard the scene-level contextual information encapsulated in feature spaces. We develop a new context-sensitive decomposition network architecture to exploit the scene-level contextual dependencies on spatial scales. We develop a lightweight CSDNet (named LiteCSDNet) by reducing the number of channels.
  arXiv  Detail & Related papers  (2021-12-09T06:25:30Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• Machine Learning Framework for Quantum Sampling of Highly-Constrained, Continuous Optimization Problems [101.18253437732933]
  We develop a generic, machine learning-based framework for mapping continuous-space inverse design problems into surrogate unconstrained binary optimization problems. We showcase the framework's performance on two inverse design problems by optimizing thermal emitter topologies for thermophotovoltaic applications and (ii) diffractive meta-gratings for highly efficient beam steering.
  arXiv  Detail & Related papers  (2021-05-06T02:22:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.