Efficient CNN-based Super Resolution Algorithms for mmWave Mobile Radar Imaging

• URL: http://arxiv.org/abs/2305.02092v1
• Date: Wed, 3 May 2023 12:54:28 GMT
• Title: Efficient CNN-based Super Resolution Algorithms for mmWave Mobile Radar Imaging
• Authors: Christos Vasileiou, Josiah W. Smith, Shiva Thiagarajan, Matthew Nigh, Yiorgos Makris, Murat Torlak
• Abstract summary: We introduce an innovative super resolution approach to emerging modes of near-field synthetic aperture radar (SAR) imaging. Recent research extends convolutional neural network (CNN) architectures to achieve super resolution on images generated from radar signaling. We propose a novel CNN architecture to achieve SAR image super-resolution for mobile applications by employing state-of-the-art SAR processing and deep learning techniques.
• Score: 2.3623206450285457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce an innovative super resolution approach to emerging modes of near-field synthetic aperture radar (SAR) imaging. Recent research extends convolutional neural network (CNN) architectures from the optical to the electromagnetic domain to achieve super resolution on images generated from radar signaling. Specifically, near-field synthetic aperture radar (SAR) imaging, a method for generating high-resolution images by scanning a radar across space to create a synthetic aperture, is of interest due to its high-fidelity spatial sensing capability, low cost devices, and large application space. Since SAR imaging requires large aperture sizes to achieve high resolution, super-resolution algorithms are valuable for many applications. Freehand smartphone SAR, an emerging sensing modality, requires irregular SAR apertures in the near-field and computation on mobile devices. Achieving efficient high-resolution SAR images from irregularly sampled data collected by freehand motion of a smartphone is a challenging task. In this paper, we propose a novel CNN architecture to achieve SAR image super-resolution for mobile applications by employing state-of-the-art SAR processing and deep learning techniques. The proposed algorithm is verified via simulation and an empirical study. Our algorithm demonstrates high-efficiency and high-resolution radar imaging for near-field scenarios with irregular scanning geometries.

Related papers

• Active Reconfigurable Intelligent Surface Empowered Synthetic Aperture Radar Imaging [18.482494583284627]
  Synthetic Aperture Radar (SAR) utilizes the movement of the radar antenna over a specific area interest to achieve higher resolution imaging. We present a range-Doppler (RD) imaging algorithm to obtain imaging results for the proposed ARIS-empowered SAR system.
  arXiv  Detail & Related papers  (2024-09-18T06:33:11Z)
• 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)
• A Vision Transformer Approach for Efficient Near-Field Irregular SAR Super-Resolution [0.0]
  We introduce a mobile-friend vision transformer (ViT) architecture to address position estimation error and perform SAR image super-resolution (SR) under irregular sampling geometries. The proposed algorithm, Mobile-SRViT, is the first to employ a ViT approach for SAR image enhancement and is validated in simulation and via empirical studies.
  arXiv  Detail & Related papers  (2023-05-03T12:25:01Z)
• Efficient 3-D Near-Field MIMO-SAR Imaging for Irregular Scanning Geometries [0.0]
  We introduce a novel algorithm for efficient near-field synthetic aperture radar (SAR) imaging for irregular scanning geometries. We propose a framework to mathematically decompose arbitrary and irregular sampling geometries and a joint solution to multistatic array imaging artifacts.
  arXiv  Detail & Related papers  (2023-05-03T12:07:21Z)
• Near-Field MIMO-ISAR Millimeter-Wave Imaging [0.0]
  In this paper, near-field mmWave imaging systems are discussed and developed. The rotational ISAR regime investigated in this paper requires rotating the target at a constant radial distance from the transceiver. Using a 77GHz mmWave radar, a high resolution three-dimensional (3-D) image can be reconstructed.
  arXiv  Detail & Related papers  (2023-05-03T10:46:48Z)
• Passive superresolution imaging of incoherent objects [63.942632088208505]
  Method consists of measuring the field's spatial mode components in the image plane in the overcomplete basis of Hermite-Gaussian modes and their superpositions. Deep neural network is used to reconstruct the object from these measurements.
  arXiv  Detail & Related papers  (2023-04-19T15:53:09Z)
• NeRF-SR: High-Quality Neural Radiance Fields using Super-Sampling [82.99453001445478]
  We present NeRF-SR, a solution for high-resolution (HR) novel view synthesis with mostly low-resolution (LR) inputs. Our method is built upon Neural Radiance Fields (NeRF) that predicts per-point density and color with a multi-layer perceptron.
  arXiv  Detail & Related papers  (2021-12-03T07:33:47Z)
• Deep Burst Super-Resolution [165.90445859851448]
  We propose a novel architecture for the burst super-resolution task. Our network takes multiple noisy RAW images as input, and generates a denoised, super-resolved RGB image as output. In order to enable training and evaluation on real-world data, we additionally introduce the BurstSR dataset.
  arXiv  Detail & Related papers  (2021-01-26T18:57:21Z)
• Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images [54.44842669325082]
  This work proposes to bridge the gap between image quality and the price by improving the image quality via super-resolution (SR) We design an SR framework that analyzes the regional information content on each patch of the low-resolution image. We show substantial decrease in inference time while achieving similar performance to that of existing deep SR methods.
  arXiv  Detail & Related papers  (2020-08-03T13:55:39Z)
• Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery [79.69449412334188]
  In this paper, we investigate how to adapt state-of-the-art residual learning based single gray/RGB image super-resolution approaches. We introduce a spatial-spectral prior network (SSPN) to fully exploit the spatial information and the correlation between the spectra of the hyperspectral data. Experimental results on some hyperspectral images demonstrate that the proposed SSPSR method enhances the details of the recovered high-resolution hyperspectral images.
  arXiv  Detail & Related papers  (2020-05-18T14:25:50Z)
• Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution [82.1739023587565]
  We propose a novel spectral-spatial residual network for hyperspectral image super-resolution (SSRNet) Our method can effectively explore spatial-spectral information by using 3D convolution instead of 2D convolution, which enables the network to better extract potential information. In each unit, we employ spatial and temporal separable 3D convolution to extract spatial and spectral information, which not only reduces unaffordable memory usage and high computational cost, but also makes the network easier to train.
  arXiv  Detail & Related papers  (2020-01-14T03:34:55Z)

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.