Sub-Rayleigh resolution of two incoherent sources by array homodyning
- URL: http://arxiv.org/abs/2005.08693v2
- Date: Fri, 27 Nov 2020 16:16:28 GMT
- Title: Sub-Rayleigh resolution of two incoherent sources by array homodyning
- Authors: Chandan Datta, Marcin Jarzyna, Yink Loong Len, Karol {\L}ukanowski,
Jan Ko{\l}ody\'nski, Konrad Banaszek
- Abstract summary: Incoherent imaging based on measuring the spatial intensity distribution in the image plane faces the resolution hurdle described by the Rayleigh diffraction criterion.
Here, we demonstrate theoretically using the concept of the Fisher information that quadrature statistics measured by means of array homodyne detection enables estimation of the distance between two incoherent point sources well below the Rayleigh limit for sufficiently high signal-to-noise ratio.
- Score: 3.6944296923226316
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Conventional incoherent imaging based on measuring the spatial intensity
distribution in the image plane faces the resolution hurdle described by the
Rayleigh diffraction criterion. Here, we demonstrate theoretically using the
concept of the Fisher information that quadrature statistics measured by means
of array homodyne detection enables estimation of the distance between two
incoherent point sources well below the Rayleigh limit for sufficiently high
signal-to-noise ratio. This capability is attributed to the availability of
spatial coherence information between individual detector pixels acquired using
the coherent detection technique. A simple analytical approximation for the
precision attainable in the sub-Rayleigh region is presented. Furthermore, an
estimation algorithm is proposed and applied to Monte Carlo simulated data.
Related papers
- Superresolution in separation estimation between two dynamic incoherent sources using spatial demultiplexing [0.0]
Recently, perfect measurement based on spatial mode demultiplexing (SPADE) in Hermite-Gauss modes allowed one to reach the quantum limit of precision for estimation of separation between two weak incoherent stationary sources.
In this paper, we consider another deviation from the perfect setup by discarding the assumption about the stationarity of the sources.
We formulate a measurement algorithm that allows for the reduction of one parameter for estimation in the stationary sources scenario.
arXiv Detail & Related papers (2024-07-15T07:57:57Z) - 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) - Fundamental Limits on Subwavelength Range Resolution [0.0]
We establish fundamental bounds on subwavelength resolution for the radar ranging problem, super radar''
For the minimal separation distance, both the direct field method and photon counting method show that the discriminability vanishes quadratically as the target separation goes to zero.
We discuss the application of maximum likelihood estimation to improve the range precision with optimal performance.
arXiv Detail & Related papers (2023-08-11T17:38:10Z) - Ultra-sensitive separation estimation of optical sources [0.0]
We implement a quantum-metrolgy-inspired approach for estimating the separation between two incoherent sources.
We demonstrate the remarkable effectiveness of demultiplexing for sub-Rayleigh separation estimation.
arXiv Detail & Related papers (2023-06-20T22:05:06Z) - Score Approximation, Estimation and Distribution Recovery of Diffusion
Models on Low-Dimensional Data [68.62134204367668]
This paper studies score approximation, estimation, and distribution recovery of diffusion models, when data are supported on an unknown low-dimensional linear subspace.
We show that with a properly chosen neural network architecture, the score function can be both accurately approximated and efficiently estimated.
The generated distribution based on the estimated score function captures the data geometric structures and converges to a close vicinity of the data distribution.
arXiv Detail & Related papers (2023-02-14T17:02:35Z) - Incorporating Texture Information into Dimensionality Reduction for
High-Dimensional Images [65.74185962364211]
We present a method for incorporating neighborhood information into distance-based dimensionality reduction methods.
Based on a classification of different methods for comparing image patches, we explore a number of different approaches.
arXiv Detail & Related papers (2022-02-18T13:17:43Z) - Density Ratio Estimation via Infinitesimal Classification [85.08255198145304]
We propose DRE-infty, a divide-and-conquer approach to reduce Density ratio estimation (DRE) to a series of easier subproblems.
Inspired by Monte Carlo methods, we smoothly interpolate between the two distributions via an infinite continuum of intermediate bridge distributions.
We show that our approach performs well on downstream tasks such as mutual information estimation and energy-based modeling on complex, high-dimensional datasets.
arXiv Detail & Related papers (2021-11-22T06:26:29Z) - Optimal control of coherent light scattering for binary decision
problems [0.0]
We present a framework to calculate and minimize the Helstrom bound using coherent probe fields with tailored spatial distributions.
We experimentally study a target located in between two disordered scattering media.
arXiv Detail & Related papers (2021-08-08T22:38:06Z) - 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) - Learning Optical Flow from a Few Matches [67.83633948984954]
We show that the dense correlation volume representation is redundant and accurate flow estimation can be achieved with only a fraction of elements in it.
Experiments show that our method can reduce computational cost and memory use significantly, while maintaining high accuracy.
arXiv Detail & Related papers (2021-04-05T21:44:00Z) - Correlation Plenoptic Imaging between Arbitrary Planes [52.77024349608834]
We show that the protocol enables to change the focused planes, in post-processing, and to achieve an unprecedented combination of image resolution and depth of field.
Results lead the way towards the development of compact designs for correlation plenoptic imaging devices based on chaotic light, as well as high-SNR plenoptic imaging devices based on entangled photon illumination.
arXiv Detail & Related papers (2020-07-23T14:26:14Z)
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.