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Boosting Convolutional Neural Networks With Middle Spectrum Grouped Convolution
Zhuo Su;Jiehua Zhang;Tianpeng Liu;Zhen Liu;Shuanghui Zhang;Matti Pietikäinen;Li Liu
This article proposes a novel module called middle spectrum grouped convolution (MSGC) for efficient deep convolutional neural networks (DCNNs) with the mechanism of grouped convolution. It explores the broad “middle spectrum” area between channel pruning and conventional grouped convolution. Compared with channel pruning, MSGC can retain most of the information from the input feature maps due to ...Show More
Sparse Aperture ISAR Imaging Based on Model-Driven SA-ISTA-Net
2024 9th International Conference on Signal and Image Processing (ICSIP)
Year: 2024 | Conference Paper |
Sparse Aperture Inverse Synthetic Aperture Radar (ISAR) imaging is a key issue of space sensing and has been widely applied in both military and civilian fields. Conventional Sparse Aperture ISAR imaging problems are usually solved by compressive sensing (CS). However, most of the CS recovery methods are computationally inefficient, and the algorithm parameters are sensitive, making it difficult t...Show More
Automatic Instantaneous Attitude Estimation of Satellite Based on Optical and ISAR Image Fusion
2024 9th International Conference on Signal and Image Processing (ICSIP)
Year: 2024 | Conference Paper |
The instantaneous attitude estimation of satellite becomes a focal topic in the increasingly significant space situation awareness area. Both the optical, inverse synthetic aperture radar (ISAR) and their fusion way have been widely applied. Due to the domain gap between two different types of data, it is difficult to make use of them for automatic attitude estimation. In view of this, this paper ...Show More
A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution
Zhixiong Yang;Jingyuan Xia;Shengxi Li;Xinghua Huang;Shuanghui Zhang;Zhen Liu;Yaowen Fu;Yongxiang Liu
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (9)
Deep learning-based methods have achieved significant successes on solving the blind super-resolution (BSR) problem. However, most of them request supervised pretraining on labelled datasets. This paper proposes an unsupervised kernel estimation model, named dynamic kernel prior (DKP), to realize an unsupervised and pretraining-free learning-based algorithm for solving the BSR problem. DKP can ada...Show More
Blind Super-Resolution via Meta-Learning and Markov Chain Monte Carlo Simulation
Jingyuan Xia;Zhixiong Yang;Shengxi Li;Shuanghui Zhang;Yaowen Fu;Deniz Gündüz;Xiang Li
Learning based approaches have witnessed great successes in blind single image super-resolution (SISR) tasks, however, handcrafted kernel priors and learning based kernel priors are typically required. In this paper, we propose a meta-learning and Markov Chain Monte Carlo (MCMC) based SISR approach to learn kernel priors from organized randomness. In concrete, a lightweight network is adopted as k...Show More
ISAR Imaging of Target Exhibiting Micro-Motion Components Based on Joint Constraints of Low Rank and Structured Sparsity
Yanbo Mai;Weidong Jiang;Shuanghui Zhang;Chi Zhang;Yongxiang Liu;Xiang Li
The inverse synthetic aperture radar (ISAR) can obtain high-resolution ISAR image of moving aerial targets. However, the micro-Doppler (m-D) interference generated by the target exhibiting micro-motion components can lead to a locally defocused ISAR image. Moreover, the echoes received by the receiver of the radar may be missing due to the environment for the propagation of electromagnetic waves a...Show More
Fast Bayesian Method for Joint Sparse ISAR Imaging and Motion Compensation for Uniform Rotating Targets
For inverse synthetic aperture radar (ISAR) imaging under sparse aperture (SA) conditions, the rotation motion compensation is seldom considered. However, with the improvement of resolution, the migration through resolution cell (MTRC) cannot be ignored. Traditional methods for rotation motion compensation generally fail in SA cases. This article proposes a method to jointly implement sparse imagi...Show More
Fast Sparse Aperture ISAR Imaging for Maneuvering Target by CZT- and NCS-Based Approximated Observation Model
Sparse aperture (SA) inverse synthetic aperture radar (ISAR) imaging for maneuvering targets is a relatively difficult task due to the complex form of observation model. In this letter, an approximated observation model based on chirp-z transform (CZT) and nonlinear chirp scaling (NCS) is proposed to accelerate the implementation of forward and backward operators. A structured sparse prior is intr...Show More
A Sparse Aperture ISAR Imaging and Autofocusing Method Based on Meta-Learning Framework
The cross-range resolution of inverse synthetic aperture radar (ISAR) images is influenced by undersampled data under the sparse aperture (SA) condition. Recently, learning-based methods have been applied to SA-ISAR imaging and have achieved impressive performance. Learning-based methods can achieve satisfactory results by training on large datasets. However, these methods may fail to reconstruct ...Show More
An Azimuth Aware Deep Reinforcement Learning Framework for Active SAR Target Recognition
Xinhua Jiang;Tianpeng Liu;Yongxiang Liu;Shuanghui Zhang;Huangxing Lin;Li Liu
Achieving automatic target recognition in synthetic aperture radar (SAR) imagery is a long-standing difficulty because of the limited training samples and its sensitivity to imaging condition. Active target recognition methods can offer an innovative perspective to improve recognition accuracy compared to their passive counterparts. Although prevailing in the optical imagery area, the active targe...Show More
Space target image deblurring via adaptive motion kernel learning
IET International Radar Conference (IRC 2023)
Year: 2023 | Conference Paper |
Image debulrring has been a challenging task in scenarios where motions occurred to the target or the sensors, such as aerospace explorations and traffic monitoring. Recent years have seen great success of deep learning in image deblurring, however, few of them are specially designed for arbitrary motion conditions, ex., space target debulrring. To solve this issue, in this paper, we propose a nov...Show More
A Multistatic ISAR Imaging Method Based on Similarity Prior With Overlaps Among Observation Angles
A multistatic inverse synthetic aperture radar (ISAR) system can observe a target from multiple observation angles. Compared with the monostatic ISAR system, the multistatic ISAR system can obtain more spatial sampling data, which provides the ability for high-resolution ISAR imaging. In some cases, the locations of radars are close. There are overlaps among observing angles, which brings little c...Show More
Azimuth RCS Reconstruction Based on SPICE Algorithm
Yingjun Li;Wenpeng Zhang;Yongxiang Liu;Shuanghui Zhang;Yaowen Fu
2023 8th International Conference on Signal and Image Processing (ICSIP)
Year: 2023 | Conference Paper |
Aiming at the problem of long measurement period and high cost in anechoic chambers, an azimuth RCS reconstruction method based on sparse iterative covariance estimation (SPICE) criterion is proposed, on the deduction that the two-dimensional (2D) geometrical theory of diffraction (GTD) scattering model can be equivalently converted into N one-dimensional (1D) undamped exponential (UE) scattering ...Show More
RaNeRF: Neural 3-D Reconstruction of Space Targets From ISAR Image Sequences
Compared to 2-D inverse synthetic aperture radar (ISAR) images of a space target, its 3-D model can provide adequate details and accurate measurement parameters. However, it is challenging to tackle the problem of feature extraction and correlation during 3-D reconstruction of space targets purely based on radar image sequences, due to their lack of clear evidence in imaging similarity compared to...Show More
ISAR Image Segmentation for Space Target Based on Contrastive Learning and NL-Unet
Peng Kou;Xiangfeng Qiu;Yongxiang Liu;Dongjie Zhao;Weijie Li;Shuanghui Zhang
The inverse synthetic aperture radar (ISAR) images are often afflicted by boundary-blurring, discontinuity, sidelobe effects of strong scattering points, a large dynamic range of gray values, and azimuth defocus, which pose significant challenges to image segmentation. This letter proposes a novel semantic segmentation method for ISAR images of space targets. The method is based on contrastive lea...Show More
ISAR Imaging of Precession Target Based on Joint Constraints of Low Rank and Sparsity of Tensor
Yanbo Mai;Shuanghui Zhang;Weidong Jiang;Chi Zhang;Kai Huo;Yongxiang Liu
Precession is a typical form of micromotion that can bring about complex and time-varying Doppler modulation. The range instantaneous Doppler (RID) method, which uses time–frequency analysis instead of the Fourier transform to describe the time-varying Doppler, is typically used to obtain the high-resolution inverse synthetic aperture radar (ISAR) image of a precession target. However, the observa...Show More
ISAR Imaging of Targets Exhibiting Micromotion Under the Joint Constraints of Low SNR and Sparse Rate
Yanbo Mai;Shuanghui Zhang;Weidong Jiang;Chi Zhang;Kai Huo;Yongxiang Liu
To solve the problem of the defocusing of inverse synthetic aperture radar (ISAR) image of targets exhibiting micromotion under the joint constraints of low signal-to-noise ratio (SNR) and sparse rate, this article proposes a method based on the joint constraints of noise and prior information of target. We use the l2 norm to eliminate the noise, and constrain prior information of target (low rank...Show More
Sparse Aperture ISAR Imaging Method Based on Meta-CV-ADMMN
2022 7th International Conference on Signal and Image Processing (ICSIP)
Year: 2022 | Conference Paper |
Sparse aperture inverse synthetic aperture radar (ISAR) imaging has been applied on both civil and military fields for its ability of achieving high resolution radar image of the moving target. The sparse aperture ISAR imaging problem is generally solved via compressed sensing (CS) algorithms using the natural sparsity of ISAR images. Recently, with the development of deep learning theory, the dee...Show More
A Learning-Aided Flexible Gradient Descent Approach to MISO Beamforming
Zhixiong Yang;Jing-Yuan Xia;Junshan Luo;Shuanghui Zhang;Deniz Gündüz
This letter proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input single-output (MISO) beamforming. The proposed LAGD algorithm directly optimizes the transmit precoder through implicit gradient descent based iterations, at each of which the optimization strategy is determined by a neural network, and thus, is dynam...Show More
ISAR Imaging of Target Exhibiting Micro-Motion With Sparse Aperture via Model-Driven Deep Network
Yanbo Mai;Shuanghui Zhang;Weidong Jiang;Chi Zhang;Yongxiang Liu;Xiang Li
This study proposes a model-driven deep network based on the linear alternating direction method of multipliers (L-ADMM), to solve the problem whereby the inverse synthetic aperture radar (ISAR) generates defocused images of targets exhibiting micro-motion with sparse aperture. The network unfolds the operation process of L-ADMM into a model-driven deep network, and automatically optimizes the par...Show More
A Computational Efficient 2-D Block-Sparse ISAR Imaging Method Based on PCSBL-GAMP-Net
Sparse aperture inverse synthesis aperture radar (SA-ISAR) imaging is generally solved by compressed sensing (CS) methods or sparse signal recovery (SSR). Many SSR methods focus on the sparsity of radar images only, which achieves unsatisfactory results on structural data. In addition, most of the traditional CS algorithms suffer from a heavy computational burden. In this article, a new deep unfol...Show More
Removal of Micro-Doppler Effect of ISAR Image Based on Laplacian Regularized Nonconvex Low-Rank Representation
The micro-Doppler (m-D) effect caused by micro-motion degrades the readability of the inverse synthetic aperture radar (ISAR) image. To achieve well-focused ISAR image of the target with the micro-motion part, this paper proposes a novel approach for the removal of m-D effect of ISAR image. Note that the range profiles of the rigid body are similar to each other, making the respective data matrix ...Show More
Micro-Doppler Effects Removed Sparse Aperture ISAR Imaging via Low-Rank and Double Sparsity Constrained ADMM and Linearized ADMM
Inverse synthetic aperture radar (ISAR) imaging for the target with micro-motion parts is influenced by the micro-Doppler (m-D) effects. In this case, the radar echo is generally decomposed into the components from the main body and micro-motion parts of target, respectively, to remove the m-D effects and derive a focused ISAR image of the main body. For the sparse aperture data, however, the rada...Show More
Enhancing ISAR Image Efficiently via Convolutional Reweighted l1 Minimization
Inverse synthetic aperture radar (ISAR) imaging for the sparse aperture data is affected by considerable artifacts, because under-sampling of data produces high-level grating and side lobes. Noting the ISAR image generally exhibits strong sparsity, it is often obtained by sparse signal recovery (SSR) in case of sparse aperture. The image obtained by SSR, however, is often dominated by strong isola...Show More
Sparse aperture radar imaging is generally achieved by methods of compressive sensing (CS), or, sparse signal recovery (SSR). However, most of the traditional SSR methods cannot produce focused image stably, which limits their applications. l1 regularization and alternating direction method of multipliers(ADMM) are generally applied to the SSR problem, but its performance is sensitive to the selec...Show More
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