Graph-CNNs for RF Imaging: Learning the Electric Field Integral Equations

• URL: http://arxiv.org/abs/2503.14439v1
• Date: Tue, 18 Mar 2025 17:16:40 GMT
• Title: Graph-CNNs for RF Imaging: Learning the Electric Field Integral Equations
• Authors: Kyriakos Stylianopoulos, Panagiotis Gavriilidis, Gabriele Gradoni, George C. Alexandropoulos,
• Abstract summary: We propose a Deep Neural Network (DNN) architecture to learn the corresponding inverse model.<n>A graph-attention backbone allows for the system geometry to be passed to the DNN, where residual convolutional layers extract features about the objects.<n>Our evaluations on two synthetic data sets of different characteristics showcase the performance gains of thee proposed advanced architecture.
• Score: 20.07924835384647
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Radio-Frequency (RF) imaging concerns the digital recreation of the surfaces of scene objects based on the scattered field at distributed receivers. To solve this difficult inverse scattering problems, data-driven methods are often employed that extract patterns from similar training examples, while offering minimal latency. In this paper, we first provide an approximate yet fast electromagnetic model, which is based on the electric field integral equations, for data generation, and subsequently propose a Deep Neural Network (DNN) architecture to learn the corresponding inverse model. A graph-attention backbone allows for the system geometry to be passed to the DNN, where residual convolutional layers extract features about the objects, while a UNet head performs the final image reconstruction. Our quantitative and qualitative evaluations on two synthetic data sets of different characteristics showcase the performance gains of thee proposed advanced architecture and its relative resilience to signal noise levels and various reception configurations.

Related papers

• From Fourier to Neural ODEs: Flow Matching for Modeling Complex Systems [20.006163951844357]
  We propose a simulation-free framework for training neural ordinary differential equations (NODEs) We employ the Fourier analysis to estimate temporal and potential high-order spatial gradients from noisy observational data. Our approach outperforms state-of-the-art methods in terms of training time, dynamics prediction, and robustness.
  arXiv  Detail & Related papers  (2024-05-19T13:15:23Z)
• GAN-driven Electromagnetic Imaging of 2-D Dielectric Scatterers [4.510838705378781]
  Inverse scattering problems are inherently challenging, given the fact they are ill-posed and nonlinear. This paper presents a powerful deep learning-based approach that relies on generative adversarial networks. A cohesive inverse neural network (INN) framework is set up comprising a sequence of appropriately designed dense layers. The trained INN demonstrates an enhanced robustness, evidenced by a mean binary cross-entropy (BCE) loss of $0.13$ and a structure similarity index (SSI) of $0.90$.
  arXiv  Detail & Related papers  (2024-02-16T17:03:08Z)
• Deep Equilibrium Diffusion Restoration with Parallel Sampling [120.15039525209106]
  Diffusion model-based image restoration (IR) aims to use diffusion models to recover high-quality (HQ) images from degraded images, achieving promising performance. Most existing methods need long serial sampling chains to restore HQ images step-by-step, resulting in expensive sampling time and high computation costs. In this work, we aim to rethink the diffusion model-based IR models through a different perspective, i.e., a deep equilibrium (DEQ) fixed point system, called DeqIR.
  arXiv  Detail & Related papers  (2023-11-20T08:27:56Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• ResFields: Residual Neural Fields for Spatiotemporal Signals [61.44420761752655]
  ResFields is a novel class of networks specifically designed to effectively represent complex temporal signals. We conduct comprehensive analysis of the properties of ResFields and propose a matrix factorization technique to reduce the number of trainable parameters. We demonstrate the practical utility of ResFields by showcasing its effectiveness in capturing dynamic 3D scenes from sparse RGBD cameras.
  arXiv  Detail & Related papers  (2023-09-06T16:59:36Z)
• Factor Fields: A Unified Framework for Neural Fields and Beyond [50.29013417187368]
  We present Factor Fields, a novel framework for modeling and representing signals. Our framework accommodates several recent signal representations including NeRF, Plenoxels, EG3D, Instant-NGP, and TensoRF. Our representation achieves better image approximation quality on 2D image regression tasks, higher geometric quality when reconstructing 3D signed distance fields, and higher compactness for radiance field reconstruction tasks.
  arXiv  Detail & Related papers  (2023-02-02T17:06:50Z)
• Deep network series for large-scale high-dynamic range imaging [2.3759432635713895]
  We propose a new approach for large-scale high-dynamic range computational imaging. Deep Neural Networks (DNNs) trained end-to-end can solve linear inverse imaging problems almost instantaneously. Alternative Plug-and-Play approaches have proven effective to address high-dynamic range challenges, but rely on highly iterative algorithms.
  arXiv  Detail & Related papers  (2022-10-28T11:13:41Z)
• All-optical graph representation learning using integrated diffractive photonic computing units [51.15389025760809]
  Photonic neural networks perform brain-inspired computations using photons instead of electrons. We propose an all-optical graph representation learning architecture, termed diffractive graph neural network (DGNN) We demonstrate the use of DGNN extracted features for node and graph-level classification tasks with benchmark databases and achieve superior performance.
  arXiv  Detail & Related papers  (2022-04-23T02:29:48Z)
• Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search [63.39818029362661]
  We present a model based on Neural Architecture Search (NAS) and self-learning for received signal strength ( RSS) map reconstruction. The approach first finds an optimal NN architecture and simultaneously train the deduced model over some ground-truth measurements of a given ( RSS) map. Experimental results show that signal predictions of this second model outperforms non-learning based state-of-the-art techniques and NN models with no architecture search.
  arXiv  Detail & Related papers  (2021-05-17T12:19:22Z)
• Noise Reduction in X-ray Photon Correlation Spectroscopy with Convolutional Neural Networks Encoder-Decoder Models [0.0]
  We propose a computational approach for improving the signal-to-noise ratio in two-time correlation functions. CNN-ED models are based on Convolutional Neural Network-Decoder (CNN-ED) models. We demonstrate that the CNN-ED models trained on real-world experimental data help to effectively extract equilibrium dynamics parameters from two-time correlation functions.
  arXiv  Detail & Related papers  (2021-02-07T18:38:59Z)
• Sparse Signal Models for Data Augmentation in Deep Learning ATR [0.8999056386710496]
  We propose a data augmentation approach to incorporate domain knowledge and improve the generalization power of a data-intensive learning algorithm. We exploit the sparsity of the scattering centers in the spatial domain and the smoothly-varying structure of the scattering coefficients in the azimuthal domain to solve the ill-posed problem of over-parametrized model fitting.
  arXiv  Detail & Related papers  (2020-12-16T21:46:33Z)
• Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies [15.2292571922932]
  We propose a novel architecture for recurrent neural networks. Our proposed RNN is based on a time-discretization of a system of second-order ordinary differential equations. Experiments show that the proposed RNN is comparable in performance to the state of the art on a variety of benchmarks.
  arXiv  Detail & Related papers  (2020-10-02T12:35:04Z)

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.