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arXiv 2021年

作者： Zhou, Wei Xu, Jiahua Jiang, Qiuping Chen, Zhibo CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China School of Information Science and Engineering Ningbo University Ningbo315211 China

360-degree/omnidirectional images (OIs) have achieved remarkable attentions due to the increasing applications of virtual reality (VR). Compared to conventional 2D images, OIs can provide more immersive experience to consumers, benefitting from the higher resolution and plentiful field of views (FoVs). Moreover, observing OIs is usually in the head mounted display (HMD) without references. Therefore, an efficient blind quality assessment method, which is specifically designed for 360-degree images, is urgently desired. In this paper, motivated by the characteristics of the human visual system (HVS) and the viewing process of VR visual contents, we propose a novel and effective no-reference omnidirectional image quality assessment (NR OIQA) algorithm by Multi-Frequency information and Local-Global Naturalness (MFILGN). Specifically, inspired by the frequency-dependent property of visual cortex, we first decompose the projected equirectangular projection (ERP) maps into wavelet subbands by using discrete Haar wavelet transform (DHWT). Then, the entropy intensities of low-frequency and high-frequency subbands are exploited to measure the multi-frequency information of OIs. Besides, except for considering the global naturalness of ERP maps, owing to the browsed FoVs, we extract the natural scene statistics (NSS) features from each viewport image as the measure of local naturalness. With the proposed multi-frequency information measurement and local-global naturalness measurement, we utilize support vector regression (SVR) as the final image quality regressor to train the quality evaluation model from visual quality-related features to human ratings. To our knowledge, the proposed model is the first no-reference quality assessment method for 360-degreee images that combines multi-frequency information and image naturalness. Experimental results on two publicly available OIQA databases demonstrate that our proposed MFILGN outperforms state-of-the-art full-reference (FR) a

关键词： Helmet mounted displays

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ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold

arXiv

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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Qiu, Xiaolan Gao, Silin Wang, Yue Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology China Suzhou Aerospace Information Research Institute Jiangsu Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Electrical and Computer Engineering Department George Mason University FairfaxVA United States

Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into TomoSAR considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this paper proposes a novel efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) architecture with adaptive threshold, named Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight matrix in each layer of ATASI-Net is pre-computed as the solution of an offline optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. In addition, adaptive threshold is introduced for each azimuth-range pixel, enabling the threshold shrinkage to be not only layer-varied but also element-wise. Moreover, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery

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Frontiers of information technology & Electronic Engineering 2019年第2期20卷 253-264页

作者： Ting-ting JIN Xiao-qiang SHE Xiao-lan QIU Bin LEI Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of ElectronicsChinese Academy of Sciences University of Chinese Academy of Sciences Huawei Software Technologies Co. Ltd.

Classification of intertidal area in synthetic aperture radar(SAR) images is an important yet challenging issue when considering the complicatedly and dramatically changing features of tidal fluctuation. The difficulty of intertidal area classification is compounded because a high proportion of this area is frequently flooded by water, making statistical modeling methods with spatial contextual information often ineffective. Because polarimetric entropy and anisotropy play significant roles in characterizing intertidal areas, in this paper we propose a novel unsupervised contextual classification algorithm. The key point of the method is to combine the generalized extreme value(GEV) statistical model of the polarization features and the Markov random field(MRF) for contextual smoothing. A goodness-of-fit test is added to determine the significance of the components of the statistical model. The final classification results are obtained by effectively combining the results of polarimetric entropy and anisotropy. Experimental results of the polarimetric data obtained by the Chinese Gaofen-3 SAR satellite demonstrate the feasibility and superiority of the proposed classification algorithm.

关键词： Intertidal classification Polarimetric synthetic aperture radar Finite mixture model Markov random field Generalized extreme value model

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A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective

arXiv

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arXiv 2021年

作者： Li, Xin Jin, Xin Fu, Jun Yu, Xiaoyuan Tong, Bei Chen, Zhibo The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China The Eastern Institute for Advanced Study China Huawei Cloud China

Collecting amounts of distorted/clean image pairs in the real world is non-trivial, which seriously limits the practical applications of these supervised learning-based methods on real-world image super-resolution (RealSR). Previous works usually address this problem by leveraging unsupervised learning-based technologies to alleviate the dependency on paired training samples. However, these methods typically suffer from unsatisfactory texture synthesis due to the lack of supervision of clean images. To overcome this problem, we are the first to have a close look at the under-explored direction for RealSR, i.e., few-shot real-world image super-resolution, which aims to tackle the challenging RealSR problem with few-shot distorted/clean image pairs. Under this brand-new scenario, we propose Distortion Relation guided Transfer Learning (DRTL) for the few-shot RealSR by transferring the rich restoration knowledge from auxiliary distortions (i.e., synthetic distortions) to the target RealSR under the guidance of distortion relation. Concretely, DRTL builds a knowledge graph to capture the distortion relation between auxiliary distortions and target distortion (i.e., real distortions in RealSR). Based on the distortion relation, DRTL adopts a gradient reweighting strategy to guide the knowledge transfer process between auxiliary distortions and target distortions. In this way, DRTL could quickly learn the most relevant knowledge from the synthetic distortions for the target distortion. We instantiate DRTL with two commonly-used transfer learning paradigms, including pre-training and meta-learning pipelines, to realize a distortion relation-aware Few-shot RealSR. Extensive experiments on multiple benchmarks and thorough ablation studies demonstrate the effectiveness of our DRTL. Copyright © 2021, The Authors. All rights reserved.

关键词： Textures

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GRIDLESS TOMOGRAPHIC SAR IMAGING BASED ON ACCELERATED ATOMIC NORM MINIMIZATION WITH EFFICIENCY

arXiv

引用

arXiv 2022年

作者： Gao, Silin Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

Synthetic aperture radar (SAR) tomography (TomoSAR) enables the reconstruction and three-dimensional (3D) localization of targets based on multiple two-dimensional (2D) observations of the same scene. The resolving along the elevation direction can be treated as a line spectrum estimation problem. However, traditional super-resolution spectrum estimation algorithms require multiple snapshots and uncorrelated targets. Meanwhile, as the most popular TomoSAR imaging method in modern years, compressed sensing (CS) based methods suffer from the gridding mismatch effect which markedly degrades the imaging performance. As a gridless CS approach, atomic norm minimization can avoid the gridding effect but requires enormous computing resources. Addressing the above issues, this paper proposes an improved fast ANM algorithm to TomoSAR elevation focusing by introducing the IVDST-ANM algorithm, which reduces the huge computational complexity of the conventional time-consuming semi-positive definite programming (SDP) by the iterative Vandermonde decomposition and shrinkage-thresholding (IVDST) approach, and retains the benefits of ANM in terms of gridless imaging and single snapshot recovery. We conducted experiments using simulated data to evaluate the performance of the proposed method, and reconstruction results of an urban area from the SARMV3D-Imaging 1.0 dataset are also presented. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

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A novel TomoSAR imaging method with few observations based on nested array

arXiv

引用

arXiv 2022年

作者： Jiang, Pengyu Zhang, Zhe Zhang, Bingchen Xu, Zhongqiu Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Suzhou Aerospace Information Research Institute Suzhou215000 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou21500 China

Synthetic aperture radar tomography (TomoSAR) baseline optimization technique is capable of reducing system complexity and improving the temporal coherence of data, which has become an important research in the field of TomoSAR. In this paper, we propose a nested TomoSAR technique, which introduces the nested array into TomoSAR as the baseline configuration. This technique obtains uniform and continuous difference co-array through nested array to increase the degrees of freedom (DoF) of the system and expands the virtual aperture along the elevation direction. In order to make full use of the difference co-array, covariance matrix of the echo needs to be obtained. Therefore, we propose a TomoSAR sparse reconstruction algorithm based on nested array, which uses adaptive covariance matrix estimation to improve the estimation performance in complex scenes. We demonstrate the effectiveness of the proposed method through simulated and real data experiments. Compared with traditional TomoSAR and coprime TomoSAR, the imaging results of our proposed method have a better anti-noise performance and retain more image information. Copyright © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

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Pattern Analysis and Placement Optimization of the Four-horn Monopulse for Terahertz Communication

Pattern Analysis and Placement Optimization of the Four-horn...

引用

Cross Strait Quad-Regional Radio Science and Wireless technology Conference (CSRWTC)

作者： Yong-Han Lu Zhong-bo Zhu Jing Ma Bin Shi Yu-ting Zhang Lin Peng Key Laboratory of Microwave and Optical Wave Application Technology Guilin University of Electronic Technology Guilin Guangxi China National Key Laboratory of Science and Technology on Space Microwave Xi'an China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China Guangxi Key Laboratory of Optoelectronic Information Processing School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin China

The mobile node communication in terahertz monopulse system requires narrow beam to align with the mobile target precisely. In this paper, the radiation pattern of four-horn monopulse antenna is analyzed by use of the geometric optics (GO) and aperture field integration method (AFIM), and an efficient placement optimization to reduce the beamwidth of four-horn monopulse is presented. The optimization combines the genetic algorithm (GA) with GO-AFIM. The GA is employed to seek the optimal positions of the four horns, and the GO and AFIM are introduced into the fitness function of GA to evaluate the individual fitness. The optimization result has narrowed the beamwidth and enhanced gain of radiation pattern. Compared with the simulation in full wave software, the proposed method costs less time in pattern analysis and performs more efficiently in placement optimization.

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Radial Velocity Ambiguity Resolution Method in Azimuth Multichannel HRWS SAR

Radial Velocity Ambiguity Resolution Method in Azimuth Multi...

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IEEE International Conference on Radar

作者： Junying Yang Xiaolan Qiu Mingyang Shang Lihua Zhong Yini Lv Chibiao Ding Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665468893

For moving targets in synthetic aperture radar (SAR) images, the obvious features are defocusing and dislocation. To estimate motion parameters accurately is a premise for the precise imaging of moving targets. However, when the radial velocity of the target exceeds the maximum detectable unambiguous velocity, the estimated value by the existing methods is no longer the real value. A radial velocity ambiguity resolution method based on Imaging Space-Time Adaptive processing (ISTAP) is proposed in this paper. The proposed method has no search or iterative process and is suitable for the azimuth multichannel SAR with low pulse repetition frequency (PRF). Finally, the simulated data from an x-band azimuth six-channel SAR system verify the feasibility of the proposed method.

关键词： Baseband Image resolution Azimuth Energy resolution Imaging Estimation Radar imaging

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Frames and vertex-frequency representations in graph fractional Fourier domain

arXiv

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arXiv 2024年

作者： Shang, Linbo Zhang, Zhichao School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University Haikou571158 China Key Laboratory of Numerical Simulation of Sichuan Provincial Universities School of Mathematics and Information Sciences Neijiang Normal University Neijiang641000 China

Vertex-frequency analysis, particularly the windowed graph Fourier transform (WGFT), is a significant challenge in graph signal processing. Tight frame theories is known for its low computational complexity in signal reconstruction, while fractional order methods shine at unveil more detailed structural characteristics of graph signals. In the graph fractional Fourier domain, we introduce multi-windowed graph fractional Fourier frames (MWGFRFF) to facilitate the construction of tight frames. This leads to developing the multi-windowed graph fractional Fourier transform (MWGFRFT), enabling novel vertex-frequency analysis methods. A reconstruction formula is derived, along with results concerning dual and tight frames. To enhance computational efficiency, a fast MWGFRFT (FMWGFRFT) algorithm is proposed. Furthermore, we define shift multi-windowed graph fractional Fourier frames (SMWGFRFF) and their associated transform (SMWGFRFT), exploring their dual and tight frames. Experimental results indicate that FMWGFRFT and SMWGFRFT excel in extracting vertex-frequency features in the graph fractional Fourier domain, with their combined use optimizing analytical performance. applications in signal anomaly detection demonstrate the advantages of FMWGFRFT. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Miniaturization Design of TEM Horn Antenna for Ground-Penetrating Radar

Miniaturization Design of TEM Horn Antenna for Ground-Penetr...

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International Conference on Microwave and Millimeter Wave technology Proceedings

作者： Zhi-Hao Li Jing Ma Jin Wu Jia-Jia Huang Yong-Han Lu Lin Peng Key Laboratory of Cognitive Radio and Information Processing (Ministry of Education) Guilin University of Electronic Technology Guilin China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Microwave and Optical Wave Application Technology (Guilin University of Electronic Technology) Guilin China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China

This paper presents a miniaturized TEM horn antenna for ground penetrating radar (GPR). First, an exponential gradient horn antenna is designed with a low cut-off frequency of 0.45 GHz. Then, the low cut-off frequency is decreased to 0.19 GHz through curve structural loading and lens loading without increasing the size of the horn. The antenna size is 378.49 mm × 359.07 mm × 386.06 mm, corresponding to 0.226 λ 1 × 0.238 λ 1 × 0.243 λ 1 ( λ 1 is the wavelength corresponding to the lowest frequency of the antenna). The results show that the working bandwidth of the antenna is 0.19-3GHz, and it has a good radiation pattern in the band, with a peak gain of 12.4dBi, which would be a good candidate for GPR.

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