Discriminative correlation tracking based on spatial attention mechanism for low-resolution imaging systems

2021 ◽  
Author(s):  
Yueping Huang ◽  
Ruitao Lu ◽  
Xiaofeng Li ◽  
Naixin Qi ◽  
Xiaogang Yang
Keyword(s):  
Spatial Attention ◽  
Attention Mechanism ◽  
Imaging Systems ◽  
Low Resolution ◽  
Correlation Tracking ◽  
Resolution Imaging

Study on Application of Super-Resolution Image Reconstruction Method Used in Video Image Processing

2012 ◽  
Vol 476-478 ◽  
pp. 1142-1145
Author(s):  
Jing Jia Qi ◽  
Chuan Jun Guo ◽  
Yang Nan
Keyword(s):  
Image Reconstruction ◽  
Performance Improvement ◽  
Super Resolution ◽  
Imaging Systems ◽  
Reconstruction Method ◽  
Low Resolution ◽  
Resolution Image ◽  
Image Reconstruction Method ◽  
Significant Performance ◽  
Resolution Imaging

Super resolution image reconstruction is a computational process of using multiple low-resolution observations to reconstruct a higher resolution image, which differs from improvement of optical devices. With magnification diversity among those low-resolution imagers, significant performance improvement, compared to traditional methods, is demonstrated. Results include fidelity metrics and simulated reconstructions. Performance improvement of super-resolution imaging systems with magnification diversity is studied in this paper.

scholarly journals Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Mengxing Huang ◽  
Liangtian Wan
Keyword(s):  
Neural Network ◽  
Continuous Wave ◽  
Super Resolution ◽  
Mimo Radar ◽  
Target Localization ◽  
Estimation Accuracy ◽  
Low Resolution ◽  
Range Estimation ◽  
Resolution Image ◽  
Resolution Imaging

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

scholarly journals Automatic Target Recognition for Low Resolution Foliage Penetrating SAR Images Using CNNs and GANs

2021 ◽  
Vol 13 (4) ◽  
pp. 596
Author(s):  
David Vint ◽  
Matthew Anderson ◽  
Yuhao Yang ◽  
Christos Ilioudis ◽  
Gaetano Di Caterina ◽  
...  
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition ◽  
Generative Adversarial Networks ◽  
Low Resolution ◽  
Sar Images ◽  
Adversarial Networks ◽  
Technological Advances ◽  
Dataset Size ◽  
Resolution Imaging ◽  
High Level

In recent years, the technological advances leading to the production of high-resolution Synthetic Aperture Radar (SAR) images has enabled more and more effective target recognition capabilities. However, high spatial resolution is not always achievable, and, for some particular sensing modes, such as Foliage Penetrating Radars, low resolution imaging is often the only option. In this paper, the problem of automatic target recognition in Low Resolution Foliage Penetrating (FOPEN) SAR is addressed through the use of Convolutional Neural Networks (CNNs) able to extract both low and high level features of the imaged targets. Additionally, to address the issue of limited dataset size, Generative Adversarial Networks are used to enlarge the training set. Finally, a Receiver Operating Characteristic (ROC)-based post-classification decision approach is used to reduce classification errors and measure the capability of the classifier to provide a reliable output. The effectiveness of the proposed framework is demonstrated through the use of real SAR FOPEN data.

Low-resolution imaging spectroscopy with SOFIA

1998 ◽  
Author(s):  
Alfred Krabbe ◽  
Juergen Wolf ◽  
Josef Schubert
Keyword(s):  
Imaging Spectroscopy ◽  
Low Resolution ◽  
Resolution Imaging

scholarly journals Phase-Based GLRT Detection of Moving Targets with Pixel Tracking in Low-Resolution SAR Image Sequences

2021 ◽  
Vol 13 (19) ◽  
pp. 3855
Author(s):  
Yulun Li ◽  
Chunsheng Li ◽  
Xiaodong Peng ◽  
Shuo Li ◽  
Hongcheng Zeng ◽  
...  
Keyword(s):  
Image Sequences ◽  
Moving Target ◽  
Ratio Test ◽  
Moving Targets ◽  
Low Resolution ◽  
Motion Signal ◽  
Resolution Imaging ◽  
Noise Statistics ◽  
The Cost ◽  
Area Monitoring

Spaceborne synthetic aperture radar (SAR) can provide ground area monitoring with large coverage. However, achieving a wide observation scope comes at the cost of resolution reduction owing to the trade-off between these parameters in conventional SAR. In low-resolution imaging, the moving target appears unresolved, weakly scattered, and slow moving in the image sequence, which can be generated by the subaperture technique. This article proposes a novel moving target detection method. First, interferometric phase statistics are combined with the generalized likelihood ratio test detector. A pixel tracking strategy is further exploited to determine whether a motion signal is present. These methods rely on the approximation of both clutter and noise statistics using Gaussian distributions in a low-resolution scenario. In addition, the motion signals are imaged with a subpixel offset. The proposed method is primarily validated using four real image sequences from TerraSAR-X data, which represent two types of homogeneous areas. The results reveal that moving targets can be detected in nearby areas using this strategy. The method is compared with the stack averaged coherence change detection and particle-filter-based tracking strategies.

UAV-based cross-view geo-localization fusion spatial attention mechanism and Netvlad

2021 ◽  
Author(s):  
Zongbao Liang ◽  
Xing Liu ◽  
Bo Chen ◽  
YunFei Yuan ◽  
Yang Song ◽  
...  
Keyword(s):  
Spatial Attention ◽  
Attention Mechanism

scholarly journals Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms

2020 ◽  
Vol 10 (12) ◽  
pp. 4312 ◽  
Author(s):  
Jie Xu ◽  
Haoliang Wei ◽  
Linke Li ◽  
Qiuru Fu ◽  
Jinhong Guo
Keyword(s):  
Neural Network ◽  
Spatial Attention ◽  
Semantic Information ◽  
Attention Mechanism ◽  
Visual Features ◽  
Feature Maps ◽  
Global Features ◽  
Model Based ◽  
Video Description ◽  
Video Visualization

Video description plays an important role in the field of intelligent imaging technology. Attention perception mechanisms are extensively applied in video description models based on deep learning. Most existing models use a temporal-spatial attention mechanism to enhance the accuracy of models. Temporal attention mechanisms can obtain the global features of a video, whereas spatial attention mechanisms obtain local features. Nevertheless, because each channel of the convolutional neural network (CNN) feature maps has certain spatial semantic information, it is insufficient to merely divide the CNN features into regions and then apply a spatial attention mechanism. In this paper, we propose a temporal-spatial and channel attention mechanism that enables the model to take advantage of various video features and ensures the consistency of visual features between sentence descriptions to enhance the effect of the model. Meanwhile, in order to prove the effectiveness of the attention mechanism, this paper proposes a video visualization model based on the video description. Experimental results show that, our model has achieved good performance on the Microsoft Video Description (MSVD) dataset and a certain improvement on the Microsoft Research-Video to Text (MSR-VTT) dataset.

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