Patch Based Transformation for Minimum Variance Beamformer Image Approximation Using Delay and Sum Pipeline

• URL: http://arxiv.org/abs/2110.10220v1
• Date: Tue, 19 Oct 2021 19:36:59 GMT
• Title: Patch Based Transformation for Minimum Variance Beamformer Image Approximation Using Delay and Sum Pipeline
• Authors: Sairoop Bodepudi, A N Madhavanunni, Mahesh Raveendranatha Panicker
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the recent past, there have been several efforts in accelerating computationally heavy beamforming algorithms such as minimum variance distortionless response (MVDR) beamforming to achieve real-time performance comparable to the popular delay and sum (DAS) beamforming. This has been achieved using a variety of neural network architectures ranging from fully connected neural networks (FCNNs), convolutional neural networks (CNNs) and general adversarial networks (GANs). However most of these approaches are working with optimizations considering image level losses and hence require a significant amount of dataset to ensure that the process of beamforming is learned. 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 (e.g. 32x32) is transformed through a U-Net architecture and multiplied with DAS apodization weights and optimized for similarity with MVDR image of the patch. Instead of framing the beamforming problem as a regression problem to estimate the apodization weights, 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. In this way, it is also observed that by restricting the input to a patch the model will learn the beamforming pipeline as an image non-linear transformation problem.

Related papers

• Adaptive Multilevel Neural Networks for Parametric PDEs with Error Estimation [0.0]
  A neural network architecture is presented to solve high-dimensional parameter-dependent partial differential equations (pPDEs) It is constructed to map parameters of the model data to corresponding finite element solutions. It outputs a coarse grid solution and a series of corrections as produced in an adaptive finite element method (AFEM)
  arXiv  Detail & Related papers  (2024-03-19T11:34:40Z)
• 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)
• 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)
• PRISTA-Net: Deep Iterative Shrinkage Thresholding Network for Coded Diffraction Patterns Phase Retrieval [6.982256124089]
  Phase retrieval is a challenge nonlinear inverse problem in computational imaging and image processing. We have developed PRISTA-Net, a deep unfolding network based on the first-order iterative threshold threshold algorithm (ISTA) All parameters in the proposed PRISTA-Net framework, including the nonlinear transformation, threshold, and step size, are learned-to-end instead of being set.
  arXiv  Detail & Related papers  (2023-09-08T07:37:15Z)
• Entropy Transformer Networks: A Learning Approach via Tangent Bundle Data Manifold [8.893886200299228]
  This paper focuses on an accurate and fast approach for image transformation employed in the design of CNN architectures. A novel Entropy STN (ESTN) is proposed that interpolates on the data manifold distributions. Experiments on challenging benchmarks show that the proposed ESTN can improve predictive accuracy over a range of computer vision tasks.
  arXiv  Detail & Related papers  (2023-07-24T04:21:51Z)
• Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
  Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
  arXiv  Detail & Related papers  (2023-03-16T21:06:13Z)
• GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction [50.248694764703714]
  Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between physics-based consistency and neural-network based regularization. We propose Greedy LEarning for Accelerated MRI reconstruction, an efficient training strategy for high-dimensional imaging settings.
  arXiv  Detail & Related papers  (2022-07-18T06:01:29Z)
• A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects [1.5708535232255898]
  Non-Cartesian trajectories need to be transformed onto a Cartesian grid in each iteration of the network training. We propose novel data augmentation methods to generate a large amount of training data from a limited number of subjects. Experimental results show superior performance of the proposed framework compared to state-of-the-art deep neural networks.
  arXiv  Detail & Related papers  (2022-06-15T00:20:22Z)
• Wide-band butterfly network: stable and efficient inversion via multi-frequency neural networks [1.2891210250935143]
  We introduce an end-to-end deep learning architecture called the wide-band butterfly network (WideBNet) for approximating the inverse scattering map from wide-band scattering data. This architecture incorporates tools from computational harmonic analysis, such as the butterfly factorization, and traditional multi-scale methods, such as the Cooley-Tukey FFT algorithm.
  arXiv  Detail & Related papers  (2020-11-24T21:48:43Z)
• A Deep-Unfolded Reference-Based RPCA Network For Video Foreground-Background Separation [86.35434065681925]
  This paper proposes a new deep-unfolding-based network design for the problem of Robust Principal Component Analysis (RPCA) Unlike existing designs, our approach focuses on modeling the temporal correlation between the sparse representations of consecutive video frames. Experimentation using the moving MNIST dataset shows that the proposed network outperforms a recently proposed state-of-the-art RPCA network in the task of video foreground-background separation.
  arXiv  Detail & Related papers  (2020-10-02T11:40:09Z)
• Deep Adaptive Inference Networks for Single Image Super-Resolution [72.7304455761067]
  Single image super-resolution (SISR) has witnessed tremendous progress in recent years owing to the deployment of deep convolutional neural networks (CNNs) In this paper, we take a step forward to address this issue by leveraging the adaptive inference networks for deep SISR (AdaDSR) Our AdaDSR involves an SISR model as backbone and a lightweight adapter module which takes image features and resource constraint as input and predicts a map of local network depth.
  arXiv  Detail & Related papers  (2020-04-08T10:08:20Z)

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.