The Marginal Importance of Distortions and Alignment in CASSI systems

• URL: http://arxiv.org/abs/2501.12705v1
• Date: Wed, 22 Jan 2025 08:20:53 GMT
• Title: The Marginal Importance of Distortions and Alignment in CASSI systems
• Authors: Léo Paillet, Antoine Rouxel, Hervé Carfantan, Simon Lacroix, Antoine Monmayrant,
• Abstract summary: This paper introduces a differentiable ray-tracing based model that incorporates aberrations and distortions to render realistic hyperspectral acquisitions using Coded-Aperture Spectral Snapshot Imagers (CASSI) Four comparable CASSI systems with varying degree of optical aberrations have been designed and modeled. The resulting rendered hyperspectral acquisitions from each of these systems are combined with five state-of-the-art hyperspectral cube reconstruction processes.
• Score: 4.3983517012036994
• License:
• Abstract: This paper introduces a differentiable ray-tracing based model that incorporates aberrations and distortions to render realistic coded hyperspectral acquisitions using Coded-Aperture Spectral Snapshot Imagers (CASSI). CASSI systems can now be optimized in order to fulfill simultaneously several optical design constraints as well as processing constraints. Four comparable CASSI systems with varying degree of optical aberrations have been designed and modeled. The resulting rendered hyperspectral acquisitions from each of these systems are combined with five state-of-the-art hyperspectral cube reconstruction processes. These reconstruction processes encompass a mapping function created from each system's propagation model to account for distortions and aberrations during the reconstruction process. Our analyses show that if properly modeled, the effects of geometric distortions of the system and misalignments of the dispersive elements have a marginal impact on the overall quality of the reconstructed hyperspectral data cubes. Therefore, relaxing traditional constraints on measurement conformity and fidelity to the scene enables the development of novel imaging instruments, guided by performance metrics applied to the design or the processing of acquisitions. By providing a complete framework for design, simulation and evaluation, this work contributes to the optimization and exploration of new CASSI systems, and more generally to the computational imaging community.

Related papers

• Deep Learning based Optical Image Super-Resolution via Generative Diffusion Models for Layerwise in-situ LPBF Monitoring [4.667646675144656]
  We implement generative deep learning models to link low-cost, low-resolution images of the build plate to detailed high-resolution optical images of the build plate. A conditional latent probabilistic diffusion model is trained to produce realistic high-resolution images of the build plate from low-resolution webcam images. We also design a framework to recreate the 3D morphology of the printed part and analyze the surface roughness of the reconstructed samples.
  arXiv  Detail & Related papers  (2024-09-20T02:59:25Z)
• Efficient One-Step Diffusion Refinement for Snapshot Compressive Imaging [8.819370643243012]
  Coded Aperture Snapshot Spectral Imaging (CASSI) is a crucial technique for capturing three-dimensional multispectral images (MSIs) Current state-of-the-art methods, predominantly end-to-end, face limitations in reconstructing high-frequency details. This paper introduces a novel one-step Diffusion Probabilistic Model within a self-supervised adaptation framework for Snapshot Compressive Imaging.
  arXiv  Detail & Related papers  (2024-09-11T17:02:10Z)
• Cross-Scan Mamba with Masked Training for Robust Spectral Imaging [51.557804095896174]
  We propose the Cross-Scanning Mamba, named CS-Mamba, that employs a Spatial-Spectral SSM for global-local balanced context encoding. Experiment results show that our CS-Mamba achieves state-of-the-art performance and the masked training method can better reconstruct smooth features to improve the visual quality.
  arXiv  Detail & Related papers  (2024-08-01T15:14:10Z)
• 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)
• ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution [76.7408734079706]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. We propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure.
  arXiv  Detail & Related papers  (2023-07-26T07:45:14Z)
• Computational Optics for Mobile Terminals in Mass Production [17.413494778377565]
  We construct the perturbed lens system model to illustrate the relationship between the system parameters and the deviated frequency response measured from photographs. An optimization framework is proposed based on this model to build proxy cameras from the machining samples' SFRs. Engaging with the proxy cameras, we synthetic data pairs, which encode the optical aberrations and the random manufacturing biases, for training the aberration-based algorithms.
  arXiv  Detail & Related papers  (2023-05-10T04:17:33Z)
• Optical Aberration Correction in Postprocessing using Imaging Simulation [17.331939025195478]
  The popularity of mobile photography continues to grow. Recent cameras have shifted some of these correction tasks from optical design to postprocessing systems. We propose a practical method for recovering the degradation caused by optical aberrations.
  arXiv  Detail & Related papers  (2023-05-10T03:20:39Z)
• Neural Lens Modeling [50.57409162437732]
  NeuroLens is a neural lens model for distortion and vignetting that can be used for point projection and ray casting. It can be used to perform pre-capture calibration using classical calibration targets, and can later be used to perform calibration or refinement during 3D reconstruction. The model generalizes across many lens types and is trivial to integrate into existing 3D reconstruction and rendering systems.
  arXiv  Detail & Related papers  (2023-04-10T20:09:17Z)
• Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution [69.11604249813304]
  We propose a components decomposition and co-optimization network (CDCN) for blind SR. CDCN decomposes the input LR image into structure and detail components in feature space. We present a degradation-driven learning strategy to jointly supervise the HR image detail and structure restoration process.
  arXiv  Detail & Related papers  (2022-12-03T14:53:56Z)
• Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images [79.07468367923619]
  We develop convolutional neural generative coding (Conv-NGC) We implement a flexible neurobiologically-motivated algorithm that progressively refines latent state maps. We study the effectiveness of our brain-inspired neural system on the tasks of reconstruction and image denoising.
  arXiv  Detail & Related papers  (2022-11-22T06:42:41Z)
• Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning [68.9515120904028]
  Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the building block.
  arXiv  Detail & Related papers  (2020-07-21T11:48:22Z)

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.