Exploring Quasi-Global Solutions to Compound Lens Based Computational Imaging Systems
- URL: http://arxiv.org/abs/2404.19201v2
- Date: Fri, 21 Feb 2025 04:05:29 GMT
- Title: Exploring Quasi-Global Solutions to Compound Lens Based Computational Imaging Systems
- Authors: Yao Gao, Qi Jiang, Shaohua Gao, Lei Sun, Kailun Yang, Kaiwei Wang,
- Abstract summary: We present Quasi-Global Search Optics (QGSO) to automatically design compound lens based computational imaging systems.<n>QGSO serves as a transformative end-to-end lens design paradigm for superior global search ability.
- Score: 15.976326291076377
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, joint design approaches that simultaneously optimize optical systems and downstream algorithms through data-driven learning have demonstrated superior performance over traditional separate design approaches. However, current joint design approaches heavily rely on the manual identification of initial lenses, posing challenges and limitations, particularly for compound lens systems with multiple potential starting points. In this work, we present Quasi-Global Search Optics (QGSO) to automatically design compound lens based computational imaging systems through two parts: (i) Fused Optimization Method for Automatic Optical Design (OptiFusion), which searches for diverse initial optical systems under certain design specifications; and (ii) Efficient Physic-aware Joint Optimization (EPJO), which conducts parallel joint optimization of initial optical systems and image reconstruction networks with the consideration of physical constraints, culminating in the selection of the optimal solution in all search results. Extensive experimental results illustrate that QGSO serves as a transformative end-to-end lens design paradigm for superior global search ability, which automatically provides compound lens based computational imaging systems with higher imaging quality compared to existing paradigms. The source code will be made publicly available at https://github.com/LiGpy/QGSO.
Related papers
- Tolerance-Aware Deep Optics [15.445359232123133]
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.
arXiv Detail & Related papers (2025-02-07T07:42:25Z) - 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 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) - Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors [52.195637608631955]
Non-line-of-sight (NLOS) imaging has attracted increasing attention due to its potential applications.
Existing NLOS reconstruction approaches are constrained by the reliance on empirical physical priors.
We introduce a novel learning-based solution, comprising two key designs: Learnable Path Compensation (LPC) and Adaptive Phasor Field (APF)
arXiv Detail & Related papers (2024-09-21T04:39:45Z) - Highly Constrained Coded Aperture Imaging Systems Design Via a Knowledge Distillation Approach [15.662108754691864]
This paper proposes a knowledge distillation (KD) framework for the design of highly physically constrained COI systems.
We validate the proposed approach, using a binary coded apertures single pixel camera for monochromatic and multispectral image reconstruction.
arXiv Detail & Related papers (2024-06-25T23:03:48Z) - Compositional Generative Inverse Design [69.22782875567547]
Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem.
We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples.
In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
arXiv Detail & Related papers (2024-01-24T01:33:39Z) - 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) - Photonic Structures Optimization Using Highly Data-Efficient Deep
Learning: Application To Nanofin And Annular Groove Phase Masks [40.11095094521714]
Metasurfaces offer a flexible framework for the manipulation of light properties in the realm of thin film optics.
This study aims to introduce a surrogate optimization framework for these devices.
The framework is applied to develop two kinds of vortex phase masks (VPMs) tailored for application in astronomical high-contrast imaging.
arXiv Detail & Related papers (2023-09-05T07:19:14Z) - Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for
Loss-free Multi-Exposure Image Fusion [60.221404321514086]
Multi-exposure image fusion (MEF) has emerged as a prominent solution to address the limitations of digital imaging in representing varied exposure levels.
This paper presents a Hybrid-Supervised Dual-Search approach for MEF, dubbed HSDS-MEF, which introduces a bi-level optimization search scheme for automatic design of both network structures and loss functions.
arXiv Detail & Related papers (2023-09-03T08:07:26Z) - Curriculum Learning for ab initio Deep Learned Refractive Optics [17.52983714236245]
DeepLens is able to learn optical designs of compound ab initio from randomlytuning surfaces without human intervention.
We demonstrate the effectiveness of our approach by fully automatically designing both classical imaging lenses and a large field-of-view extended depth-of-field computational lens.
arXiv Detail & Related papers (2023-02-02T13:22:18Z) - Deep Optical Coding Design in Computational Imaging [16.615106763985942]
Computational optical imaging (COI) systems leverage optical coding elements (CE) in their setups to encode a high-dimensional scene in a single or multiple snapshots and decode it by using computational algorithms.
The performance of COI systems highly depends on the design of its main components: the CE pattern and the computational method used to perform a given task.
Deep neural networks (DNNs) have opened a new horizon in CE data-driven designs that jointly consider the optical encoder and computational decoder.
arXiv Detail & Related papers (2022-06-27T04:41:48Z) - Image-specific Convolutional Kernel Modulation for Single Image
Super-resolution [85.09413241502209]
In this issue, we propose a novel image-specific convolutional modulation kernel (IKM)
We exploit the global contextual information of image or feature to generate an attention weight for adaptively modulating the convolutional kernels.
Experiments on single image super-resolution show that the proposed methods achieve superior performances over state-of-the-art methods.
arXiv Detail & Related papers (2021-11-16T11:05:10Z) - RRNet: Relational Reasoning Network with Parallel Multi-scale Attention
for Salient Object Detection in Optical Remote Sensing Images [82.1679766706423]
Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs.
We propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs.
Our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively.
arXiv Detail & Related papers (2021-10-27T07:18:32Z) - A Framework for Discovering Optimal Solutions in Photonic Inverse Design [0.0]
Photonic inverse design has emerged as an indispensable engineering tool for complex optical systems.
Finding solutions approaching global optimum may present a computationally intractable task.
We develop a framework that allows expediting the search of solutions close to global optimum on complex optimization spaces.
arXiv Detail & Related papers (2021-06-03T22:11:03Z) - Universal and Flexible Optical Aberration Correction Using Deep-Prior
Based Deconvolution [51.274657266928315]
We propose a PSF aware plug-and-play deep network, which takes the aberrant image and PSF map as input and produces the latent high quality version via incorporating lens-specific deep priors.
Specifically, we pre-train a base model from a set of diverse lenses and then adapt it to a given lens by quickly refining the parameters.
arXiv Detail & Related papers (2021-04-07T12:00:38Z) - Optimization-Inspired Learning with Architecture Augmentations and
Control Mechanisms for Low-Level Vision [74.9260745577362]
This paper proposes a unified optimization-inspired learning framework to aggregate Generative, Discriminative, and Corrective (GDC) principles.
We construct three propagative modules to effectively solve the optimization models with flexible combinations.
Experiments across varied low-level vision tasks validate the efficacy and adaptability of GDC.
arXiv Detail & Related papers (2020-12-10T03:24:53Z) - End-to-end Full Projector Compensation [81.19324259967742]
Full projector compensation aims to modify a projector input image to compensate for both geometric and photometric disturbance of the projection surface.
In this paper, we propose the first end-to-end differentiable solution, named CompenNeSt++, to solve the two problems jointly.
arXiv Detail & Related papers (2020-07-30T18:23:52Z) - A Flexible Framework for Designing Trainable Priors with Adaptive
Smoothing and Game Encoding [57.1077544780653]
We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems.
We focus on convex games, solved by local agents represented by the nodes of a graph and interacting through regularization functions.
This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end.
arXiv Detail & Related papers (2020-06-26T08:34:54Z) - Two-shot Spatially-varying BRDF and Shape Estimation [89.29020624201708]
We propose a novel deep learning architecture with a stage-wise estimation of shape and SVBRDF.
We create a large-scale synthetic training dataset with domain-randomized geometry and realistic materials.
Experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images.
arXiv Detail & Related papers (2020-04-01T12:56:13Z)
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.