Imaging arbitrary incoherent source distributions with near
quantum-limited resolution
- URL: http://arxiv.org/abs/2106.13332v2
- Date: Fri, 25 Feb 2022 18:55:11 GMT
- Title: Imaging arbitrary incoherent source distributions with near
quantum-limited resolution
- Authors: Erik F. Matlin and Lucas J. Zipp
- Abstract summary: We demonstrate an approach to obtaining near quantum-limited far-field imaging resolution of incoherent sources with arbitrary distributions.
Our method assumes no prior knowledge of the source distribution, but rather uses an adaptive approach to imaging via spatial mode demultiplexing.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We demonstrate an approach to obtaining near quantum-limited far-field
imaging resolution of incoherent sources with arbitrary distributions. Our
method assumes no prior knowledge of the source distribution, but rather uses
an adaptive approach to imaging via spatial mode demultiplexing that
iteratively updates both the form of the spatial imaging modes and the estimate
of the source distribution. The optimal imaging modes are determined by
minimizing the estimated Cram\'er-Rao bound over the manifold of all possible
sets of orthogonal imaging modes. We have observed through Monte Carlo
simulations that the manifold-optimized spatial mode demultiplexing measurement
consistently outperforms standard imaging techniques in the accuracy of source
reconstructions and comes within a factor of 2 of the absolute quantum limit as
set by the quantum Cram\'er-Rao bound. The adaptive framework presented here
allows for a consistent approach to achieving near quantum-limited imaging
resolution of arbitrarily distributed sources through spatial mode imaging
techniques.
Related papers
- Advancing quantum imaging through learning theory [7.19995826332098]
We quantify performance of quantum imaging by modeling it as a learning task and calculating the Resolvable Expressive Capacity (REC)
We first examine imaging performance for two-point sources and generally distributed sources, referred to as compact sources.
arXiv Detail & Related papers (2025-01-26T22:02:13Z) - Arbitrary-steps Image Super-resolution via Diffusion Inversion [68.78628844966019]
This study presents a new image super-resolution (SR) technique based on diffusion inversion, aiming at harnessing the rich image priors encapsulated in large pre-trained diffusion models to improve SR performance.
We design a Partial noise Prediction strategy to construct an intermediate state of the diffusion model, which serves as the starting sampling point.
Once trained, this noise predictor can be used to initialize the sampling process partially along the diffusion trajectory, generating the desirable high-resolution result.
arXiv Detail & Related papers (2024-12-12T07:24:13Z) - Distributed Markov Chain Monte Carlo Sampling based on the Alternating
Direction Method of Multipliers [143.6249073384419]
In this paper, we propose a distributed sampling scheme based on the alternating direction method of multipliers.
We provide both theoretical guarantees of our algorithm's convergence and experimental evidence of its superiority to the state-of-the-art.
In simulation, we deploy our algorithm on linear and logistic regression tasks and illustrate its fast convergence compared to existing gradient-based methods.
arXiv Detail & Related papers (2024-01-29T02:08:40Z) - MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network [65.1004435124796]
We propose a Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network (MB-RACS) framework.
Our experiments demonstrate that the proposed MB-RACS method surpasses current leading methods.
arXiv Detail & Related papers (2024-01-19T04:40:20Z) - SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired
Image-to-Image Translation [96.11061713135385]
This work presents a new score-decomposed diffusion model to explicitly optimize the tangled distributions during image generation.
We equalize the refinement parts of the score function and energy guidance, which permits multi-objective optimization on the manifold.
SDDM outperforms existing SBDM-based methods with much fewer diffusion steps on several I2I benchmarks.
arXiv Detail & Related papers (2023-08-04T06:21:57Z) - Quantum super-resolution for imaging two pointlike entangled photon
sources [9.590696922408775]
We investigate the resolution for imaging two pointlike entangled sources by using the method of the moments and the spatial-mode demultiplexing (SPADE)
We demonstrate that the separation estimation sensitivity is mainly determined by the photon distribution in each detected modes.
In the limiting case of infinitely small source separation, the usage of entangled sources can have better resolution than those using incoherent and coherent sources.
arXiv Detail & Related papers (2023-06-17T02:39:47Z) - Towards Accurate Post-training Quantization for Diffusion Models [73.19871905102545]
We propose an accurate data-free post-training quantization framework of diffusion models (ADP-DM) for efficient image generation.
Our method outperforms the state-of-the-art post-training quantization of diffusion model by a sizable margin with similar computational cost.
arXiv Detail & Related papers (2023-05-30T04:00:35Z) - DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion [144.9653045465908]
We propose a novel fusion algorithm based on the denoising diffusion probabilistic model (DDPM)
Our approach yields promising fusion results in infrared-visible image fusion and medical image fusion.
arXiv Detail & Related papers (2023-03-13T04:06:42Z) - Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation [2.5556910002263984]
Score-based diffusion models (SBDM) have emerged as state-of-the-art approaches for image generation.
This paper develops SBDMs in the infinite-dimensional setting, that is, we model the training data as functions supported on a rectangular domain.
We demonstrate how to overcome two shortcomings of current SBDM approaches in the infinite-dimensional setting.
arXiv Detail & Related papers (2023-03-08T18:10:10Z) - Sub-Rayleigh characterization of a binary source by spatially
demultiplexed coherent detection [3.9146761527401424]
An algorithm to estimate parameters of a two-dimensional symmetric binary source is devised and verified using Monte Carlo simulations.
The presented algorithm is shown to make a nearly optimal use of the measured data in the sub-Rayleigh region.
arXiv Detail & Related papers (2021-06-15T16:10:19Z)
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.