Reinforced Inverse Scattering
- URL: http://arxiv.org/abs/2206.04186v1
- Date: Wed, 8 Jun 2022 22:56:09 GMT
- Title: Reinforced Inverse Scattering
- Authors: Hanyang Jiang, Yuehaw Khoo, Haizhao Yang
- Abstract summary: Inverse wave scattering aims at determining the properties of an object using data on how the object scatters incoming waves.
The choice of sensor positions and incident wave frequencies determines the reconstruction quality of scatterer properties.
This paper introduces reinforcement learning to develop precision imaging that decides sensor positions and wave frequencies adaptive to different scatterers.
- Score: 2.535271349350579
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Inverse wave scattering aims at determining the properties of an object using
data on how the object scatters incoming waves. In order to collect
information, sensors are put in different locations to send and receive waves
from each other. The choice of sensor positions and incident wave frequencies
determines the reconstruction quality of scatterer properties. This paper
introduces reinforcement learning to develop precision imaging that decides
sensor positions and wave frequencies adaptive to different scatterers in an
intelligent way, thus obtaining a significant improvement in reconstruction
quality with limited imaging resources. Extensive numerical results will be
provided to demonstrate the superiority of the proposed method over existing
methods.
Related papers
- bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction [57.199618102578576]
We propose bit2bit, a new method for reconstructing high-quality image stacks at original resolution from sparse binary quantatemporal image data.
Inspired by recent work on Poisson denoising, we developed an algorithm that creates a dense image sequence from sparse binary photon data.
We present a novel dataset containing a wide range of real SPAD high-speed videos under various challenging imaging conditions.
arXiv Detail & Related papers (2024-10-30T17:30:35Z) - Wavelet-based Bi-dimensional Aggregation Network for SAR Image Change Detection [53.842568573251214]
Experimental results on three SAR datasets demonstrate that our WBANet significantly outperforms contemporary state-of-the-art methods.
Our WBANet achieves 98.33%, 96.65%, and 96.62% of percentage of correct classification (PCC) on the respective datasets.
arXiv Detail & Related papers (2024-07-18T04:36:10Z) - Redefining Automotive Radar Imaging: A Domain-Informed 1D Deep Learning Approach for High-Resolution and Efficient Performance [6.784861785632841]
Our study redefines radar imaging super-resolution as a one-dimensional (1D) signal super-resolution spectra estimation problem.
Our tailored deep learning network for automotive radar imaging exhibits remarkable scalability, parameter efficiency and fast inference speed.
Our SR-SPECNet sets a new benchmark in producing high-resolution radar range-azimuth images.
arXiv Detail & Related papers (2024-06-11T16:07:08Z) - Adaptive LPD Radar Waveform Design with Generative Deep Learning [6.21540494241516]
We propose a novel, learning-based method for adaptively generating low probability of detection radar waveforms.
Our method can generate LPD waveforms that reduce detectability by up to 90% while simultaneously offering improved ambiguity function (sensing) characteristics.
arXiv Detail & Related papers (2024-03-18T21:07:57Z) - Learning Heavily-Degraded Prior for Underwater Object Detection [59.5084433933765]
This paper seeks transferable prior knowledge from detector-friendly images.
It is based on statistical observations that, the heavily degraded regions of detector-friendly (DFUI) and underwater images have evident feature distribution gaps.
Our method with higher speeds and less parameters still performs better than transformer-based detectors.
arXiv Detail & Related papers (2023-08-24T12:32:46Z) - A physically-informed Deep-Learning approach for locating sources in a
waveguide [0.688204255655161]
Inverse source problems are central to many applications in acoustics, geophysics, non-destructive testing, and more.
Traditional imaging methods suffer from the resolution limit, preventing distinction of sources separated by less than the emitted wavelength.
We propose a method based on physically-informed neural-networks for solving the source refocusing problem.
arXiv Detail & Related papers (2022-08-07T19:54:10Z) - Toward deep-learning-assisted spectrally-resolved imaging of magnetic
noise [52.77024349608834]
We implement a deep neural network to efficiently reconstruct the spectral density of the underlying fluctuating magnetic field.
These results create opportunities for the application of machine-learning methods to color-center-based nanoscale sensing and imaging.
arXiv Detail & Related papers (2022-08-01T19:18:26Z) - On the Optimization of Underwater Quantum Key Distribution Systems with
Time-Gated SPADs [13.401746329218017]
We study the effect of various transmitter and receiver parameters on the quantum bit error rate (QBER) performance of underwater quantum key distribution.
We utilize a Monte Carlo approach to simulate the trajectories of emitted photons transmitting in the water from the transmitter towards receiver.
arXiv Detail & Related papers (2022-06-09T17:22:02Z) - Radar-based Materials Classification Using Deep Wavelet Scattering
Transform: A Comparison of Centimeter vs. Millimeter Wave Units [0.0]
This research considers two radar units with different frequency ranges: Walabot-3D (6.3-8 GHz) cm-wave and IMAGEVK-74 (62-69 GHz) mm-wave imaging units by Vayyar Imaging.
arXiv Detail & Related papers (2022-02-08T02:07:14Z) - Location-aware Single Image Reflection Removal [54.93808224890273]
This paper proposes a novel location-aware deep learning-based single image reflection removal method.
We use a reflection confidence map as the cues for the network to learn how to encode the reflection information adaptively.
The integration of location information into the network significantly improves the quality of reflection removal results.
arXiv Detail & Related papers (2020-12-13T19:34:35Z) - Depth Estimation from Monocular Images and Sparse Radar Data [93.70524512061318]
In this paper, we explore the possibility of achieving a more accurate depth estimation by fusing monocular images and Radar points using a deep neural network.
We find that the noise existing in Radar measurements is one of the main key reasons that prevents one from applying the existing fusion methods.
The experiments are conducted on the nuScenes dataset, which is one of the first datasets which features Camera, Radar, and LiDAR recordings in diverse scenes and weather conditions.
arXiv Detail & Related papers (2020-09-30T19:01:33Z)
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.