VBIM-Net: Variational Born Iterative Network for Inverse Scattering Problems

• URL: http://arxiv.org/abs/2405.18731v4
• Date: Sun, 02 Feb 2025 13:24:06 GMT
• Title: VBIM-Net: Variational Born Iterative Network for Inverse Scattering Problems
• Authors: Ziqing Xing, Zhaoyang Zhang, Zirui Chen, Yusong Wang, Haoran Ma, Zhun Wei,
• Abstract summary: We propose a novel Variational Born Iterative Network, namely, V BIM-Net, to solve the full-wave inverse scattering problems.<n>The proposed V BIM-Net emulates the alternating updates of the total electric field and the contrast in the variational Born iterative method.
• Score: 9.425567496713366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, studies have shown the potential of integrating field-type iterative methods with deep learning (DL) techniques in solving inverse scattering problems (ISPs). In this article, we propose a novel Variational Born Iterative Network, namely, VBIM-Net, to solve the full-wave ISPs with significantly improved structural rationality and inversion quality. The proposed VBIM-Net emulates the alternating updates of the total electric field and the contrast in the variational Born iterative method (VBIM) by multiple layers of subnetworks. We embed the analytical calculation of the contrast variation into each subnetwork, converting the scattered field residual into an approximate contrast variation and then enhancing it by a U-Net, thus avoiding the requirement of matched measurement dimension and grid resolution as in existing approaches. The total field and contrast of each layer's output is supervised in the loss function of VBIM-Net, imposing soft physical constraints on the variables in the subnetworks, which benefits the model's performance. In addition, we design a training scheme with extra noise to enhance the model's stability. Extensive numerical results on synthetic and experimental data both verify the inversion quality, generalization ability, and robustness of the proposed VBIM-Net. This work may provide some new inspiration for the design of efficient field-type DL schemes.

Related papers

• Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems [11.456980278006574]
  This paper presents an improved physics-driven neural network (IPDNN) framework for solving electromagnetic inverse scattering problems (ISPs)<n>A new Gaussian-localized oscillation-suppressing window (GLOW) activation function is introduced to stabilize convergence and enable a lightweight yet accurate network architecture.<n>The proposed solver achieves superior reconstruction accuracy, robustness, and efficiency compared with existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-12-10T05:41:09Z)
• Robust Variational Model Based Tailored UNet: Leveraging Edge Detector and Mean Curvature for Improved Image Segmentation [7.638424494500011]
  This paper presents a robust version of Variational Model Based UNet (VM_TUNet)<n>VM_TUNet is a hybrid framework that integrates variational methods with deep learning.<n> experiments on three benchmark datasets indicate that the proposed method achieves a balanced trade-off between performance and computational efficiency.
  arXiv  Detail & Related papers  (2025-12-08T14:33:52Z)
• Multi-resolution Physics-Aware Recurrent Convolutional Neural Network for Complex Flows [2.7233737247962786]
  MRPARCv2 is designed to model complex flows by embedding the structure of advection-diffusion-reaction equations.<n>We evaluate the model on a challenging 2D turbulent radiative layer dataset from The Well multi-physics benchmark repository.
  arXiv  Detail & Related papers  (2025-12-04T16:19:10Z)
• BHViT: Binarized Hybrid Vision Transformer [53.38894971164072]
  Model binarization has made significant progress in enabling real-time and energy-efficient computation for convolutional neural networks (CNN) We propose BHViT, a binarization-friendly hybrid ViT architecture and its full binarization model with the guidance of three important observations. Our proposed algorithm achieves SOTA performance among binary ViT methods.
  arXiv  Detail & Related papers  (2025-03-04T08:35:01Z)
• Preconditioned Inexact Stochastic ADMM for Deep Model [35.37705488695026]
  This paper develops an algorithm, PISA, which enables scalable parallel computing and supports various second-moment schemes. Grounded in rigorous theoretical guarantees, the algorithm converges under the sole assumption of Lipschitz of the gradient. Comprehensive experimental evaluations for or fine-tuning diverse FMs, including vision models, large language models, reinforcement learning models, generative adversarial networks, and recurrent neural networks, demonstrate its superior numerical performance compared to various state-of-the-art Directions.
  arXiv  Detail & Related papers  (2025-02-15T12:28:51Z)
• A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites [5.896656636095934]
  MMIMO technology has transformed wireless communication by enhancing spectral efficiency and network capacity. This paper proposes a novel deep learning-based mMIMO precoder to tackle the complexity challenges of existing approaches.
  arXiv  Detail & Related papers  (2025-02-12T20:02:36Z)
• Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware [78.17783007774295]
  This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval. A novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed. The converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
  arXiv  Detail & Related papers  (2024-12-05T09:41:33Z)
• Unleashing Network Potentials for Semantic Scene Completion [50.95486458217653]
  This paper proposes a novel SSC framework - Adrial Modality Modulation Network (AMMNet) AMMNet introduces two core modules: a cross-modal modulation enabling the interdependence of gradient flows between modalities, and a customized adversarial training scheme leveraging dynamic gradient competition. Extensive experimental results demonstrate that AMMNet outperforms state-of-the-art SSC methods by a large margin.
  arXiv  Detail & Related papers  (2024-03-12T11:48:49Z)
• Dynamically configured physics-informed neural network in topology optimization applications [4.403140515138818]
  The physics-informed neural network (PINN) can avoid generating enormous amounts of data when solving forward problems. A dynamically configured PINN-based topology optimization (DCPINN-TO) method is proposed. The accuracy of the displacement prediction and optimization results indicate that the DCPINN-TO method is effective and efficient.
  arXiv  Detail & Related papers  (2023-12-12T05:35:30Z)
• Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding [57.71603937699949]
  We study optimization guarantees, i.e., achieving near-zero training loss with the increase in the number of learning epochs. We show that the threshold on the number of training samples increases with the increase in the network width.
  arXiv  Detail & Related papers  (2023-09-12T13:03:47Z)
• A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading [62.34538208323411]
  We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs) MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
  arXiv  Detail & Related papers  (2023-09-02T11:01:16Z)
• Measurement-Consistent Networks via a Deep Implicit Layer for Solving Inverse Problems [0.0]
  End-to-end deep neural networks (DNNs) have become state-of-the-art (SOTA) for solving inverse problems. These networks are sensitive to minor variations in the training pipeline and often fail to reconstruct small but important details. We propose a framework that transforms any DNN for inverse problems into a measurement-consistent one.
  arXiv  Detail & Related papers  (2022-11-06T17:05:04Z)
• Probabilistic partition of unity networks for high-dimensional regression problems [1.0227479910430863]
  We explore the partition of unity network (PPOU-Net) model in the context of high-dimensional regression problems. We propose a general framework focusing on adaptive dimensionality reduction. The PPOU-Nets consistently outperform the baseline fully-connected neural networks of comparable sizes in numerical experiments.
  arXiv  Detail & Related papers  (2022-10-06T06:01:36Z)
• Patch Based Transformation for Minimum Variance Beamformer Image Approximation Using Delay and Sum Pipeline [0.0]
  In this work, a patch level U-Net based neural network is proposed, where the delay compensated radio frequency (RF) patch for a fixed region in space is transformed through a U-Net architecture. The proposed approach treats the non-linear transformation of the RF data space that can account for the data driven weight adaptation done by the MVDR approach in the parameters of the network.
  arXiv  Detail & Related papers  (2021-10-19T19:36:59Z)
• Physics and Equality Constrained Artificial Neural Networks: Application to Partial Differential Equations [1.370633147306388]
  Physics-informed neural networks (PINNs) have been proposed to learn the solution of partial differential equations (PDE) Here, we show that this specific way of formulating the objective function is the source of severe limitations in the PINN approach. We propose a versatile framework that can tackle both inverse and forward problems.
  arXiv  Detail & Related papers  (2021-09-30T05:55:35Z)
• ResNet-LDDMM: Advancing the LDDMM Framework Using Deep Residual Networks [86.37110868126548]
  In this work, we make use of deep residual neural networks to solve the non-stationary ODE (flow equation) based on a Euler's discretization scheme. We illustrate these ideas on diverse registration problems of 3D shapes under complex topology-preserving transformations.
  arXiv  Detail & Related papers  (2021-02-16T04:07:13Z)
• Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth [51.40441097625201]
  We propose an end-to-end trainable deep learning architecture for sparse signal recovery problems. The proposed method learns how many layers to execute to emit an output, and the network depth is dynamically adjusted for each task in the inference phase.
  arXiv  Detail & Related papers  (2020-10-29T06:32:53Z)
• Neural Control Variates [71.42768823631918]
  We show that a set of neural networks can face the challenge of finding a good approximation of the integrand. We derive a theoretically optimal, variance-minimizing loss function, and propose an alternative, composite loss for stable online training in practice. Specifically, we show that the learned light-field approximation is of sufficient quality for high-order bounces, allowing us to omit the error correction and thereby dramatically reduce the noise at the cost of negligible visible bias.
  arXiv  Detail & Related papers  (2020-06-02T11:17:55Z)

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.