Rethinking the modeling of the instrumental response of telescopes with
a differentiable optical model
- URL: http://arxiv.org/abs/2111.12541v1
- Date: Wed, 24 Nov 2021 15:24:06 GMT
- Title: Rethinking the modeling of the instrumental response of telescopes with
a differentiable optical model
- Authors: Tobias Liaudat and Jean-Luc Starck and Martin Kilbinger and
Pierre-Antoine Frugier
- Abstract summary: We propose a paradigm shift in the data-driven modeling of the instrumental response field of telescopes.
By adding a differentiable optical forward model into the modeling framework, we change the data-driven modeling space from the pixels to the wavefront.
Our framework allows a way forward to building powerful models that are physically motivated, interpretable, and that do not require special calibration data.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a paradigm shift in the data-driven modeling of the instrumental
response field of telescopes. By adding a differentiable optical forward model
into the modeling framework, we change the data-driven modeling space from the
pixels to the wavefront. This allows to transfer a great deal of complexity
from the instrumental response into the forward model while being able to adapt
to the observations, remaining data-driven. Our framework allows a way forward
to building powerful models that are physically motivated, interpretable, and
that do not require special calibration data. We show that for a simplified
setting of a space telescope, this framework represents a real performance
breakthrough compared to existing data-driven approaches with reconstruction
errors decreasing 5 fold at observation resolution and more than 10 fold for a
3x super-resolution. We successfully model chromatic variations of the
instrument's response only using noisy broad-band in-focus observations.
Related papers
- SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models [85.67096251281191]
We present an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction.
SMILE allows for the upscaling of source models into an MoE model without extra data or further training.
We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning.
arXiv Detail & Related papers (2024-08-19T17:32:15Z) - Unlocking the Secrets of Linear Complexity Sequence Model from A Unified Perspective [26.479602180023125]
The Linear Complexity Sequence Model (LCSM) unites various sequence modeling techniques with linear complexity.
We segment the modeling processes of these models into three distinct stages: Expand, Oscillation, and Shrink.
We perform experiments to analyze the impact of different stage settings on language modeling and retrieval tasks.
arXiv Detail & Related papers (2024-05-27T17:38:55Z) - D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction [74.49121940466675]
We introduce centroid-fixed dual-stream conditional diffusion for monocular hand-held object reconstruction.
First, to avoid the object centroid from deviating, we utilize a novel hand-constrained centroid fixing paradigm.
Second, we introduce a dual-stream denoiser to semantically and geometrically model hand-object interactions.
arXiv Detail & Related papers (2023-11-23T20:14:50Z) - OtterHD: A High-Resolution Multi-modality Model [57.16481886807386]
OtterHD-8B is an innovative multimodal model engineered to interpret high-resolution visual inputs with granular precision.
Our study highlights the critical role of flexibility and high-resolution input capabilities in large multimodal models.
arXiv Detail & Related papers (2023-11-07T18:59:58Z) - Refined Equivalent Pinhole Model for Large-scale 3D Reconstruction from
Spaceborne CCD Imagery [1.4019041243188557]
We present a large-scale earth surface reconstruction pipeline for linear-array charge-coupled satellite imagery.
Results demonstrated that the reconstruction accuracy was proportional to the image size.
Our image refinement model significantly enhanced the accuracy and completeness of the reconstruction.
arXiv Detail & Related papers (2023-10-31T01:30:57Z) - Point spread function modelling for astronomical telescopes: a review
focused on weak gravitational lensing studies [2.967246997200238]
The accurate modelling of the Point Spread Function (PSF) is of paramount importance in astronomical observations.
This review introduces the optical background required for a more physically-tightening PSF modelling.
We provide an overview of the different physical contributors of the PSF, including the optic- and detector-level contributors and the atmosphere.
arXiv Detail & Related papers (2023-06-12T19:01:50Z) - 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) - VTAE: Variational Transformer Autoencoder with Manifolds Learning [144.0546653941249]
Deep generative models have demonstrated successful applications in learning non-linear data distributions through a number of latent variables.
The nonlinearity of the generator implies that the latent space shows an unsatisfactory projection of the data space, which results in poor representation learning.
We show that geodesics and accurate computation can substantially improve the performance of deep generative models.
arXiv Detail & Related papers (2023-04-03T13:13:19Z) - Rethinking data-driven point spread function modeling with a
differentiable optical model [0.19947949439280027]
In astronomy, upcoming space telescopes with wide-field optical instruments have a spatially varying point spread function (PSF)
Current data-driven PSF models can tackle spatial variations and super-resolution, but are not capable of capturing chromatic variations.
By adding a differentiable optical forward model into the modeling framework, we change the data-driven modeling space from the pixels to the wavefront.
arXiv Detail & Related papers (2022-03-09T17:39:18Z) - PaMIR: Parametric Model-Conditioned Implicit Representation for
Image-based Human Reconstruction [67.08350202974434]
We propose Parametric Model-Conditioned Implicit Representation (PaMIR), which combines the parametric body model with the free-form deep implicit function.
We show that our method achieves state-of-the-art performance for image-based 3D human reconstruction in the cases of challenging poses and clothing types.
arXiv Detail & Related papers (2020-07-08T02:26:19Z) - Variational State-Space Models for Localisation and Dense 3D Mapping in
6 DoF [17.698319441265223]
We solve the problem of 6-DoF localisation and 3D dense reconstruction in spatial environments as approximate Bayesian inference in a deep state-space model.
This results in an expressive predictive model of the world, often missing in current state-of-the-art visual SLAM solutions.
We evaluate our approach on realistic unmanned aerial vehicle flight data, nearing the performance of state-of-the-art visual-inertial odometry systems.
arXiv Detail & Related papers (2020-06-17T22:06:35Z)
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.