Author details
Author's Published Works
Scheduled Maintenance on Monday 1/13/2025
Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.
Author details
Showing 1-12 of 12 results
- Sort
Sort By
Results
A Cross-Modal Multi-Attitude Framework for the Generation of Space Target ISAR Images
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Inverse Synthetic Aperture Radar (ISAR) imagery of space targets exhibits superior physical fidelity and satisfactory textural representation of components in ISAR images of targets, even under conditions characterized by sparse input optical samples. This paper introduces an innovative optical-to-radar cross-modal framework for the generation of full-attitude, high-fidelity space target ISAR samp...Show More
Unsupervised Hyperspectral Image Blind Super-Resolution via a Kernel Prior Network
Tong Qiu;Xinyu Zhang;Zhixiong Yang;Huaizhang Liao;Jingyuan Xia
2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)
Year: 2024 | Conference Paper |
Hyperspectral images typically suffer from low resolution due to inherent hardware constraints, complicating downstream tasks like detection, classification, and recognition. In our study, we introduce a kernel prior network (KPNet) designed to address the the hyperspectral image (HSI) blind super-resolution (BSR) challenge in an unsupervised fashion. Double-DIP, comprising two deep image priors (...Show More
Supervised Contrastive Learning for Long-tailed SAR-AIRcraft Image Classification
2024 9th International Conference on Image, Vision and Computing (ICIVC)
Year: 2024 | Conference Paper |
Deep learning has witnessed revolutionary successes in the area of Synthetic Aperture Radar (SAR) image classification. However, most of the existing methods do not perform well on long-tailed dataset, etc., the number of samples for different categories are imbalanced. Meanwhile, in real-world SAR classification tasks, it is common to face classification tasks that the interesting targets are mer...Show More
Meta-Learning Based Domain Prior With Application to Optical-ISAR Image Translation
Huaizhang Liao;Jingyuan Xia;Zhixiong Yang;Fulin Pan;Zhen Liu;Yongxiang Liu
This paper focuses on generating Inverse Synthetic Aperture Radar (ISAR) images from optical images, in particular, for orbit space targets. ISAR images are widely applied in space target observation and classification tasks, whereas, limited to the expensive cost of ISAR sample collection, training deep learning-based ISAR image classifiers with insufficient samples and generating ISAR samples fr...Show More
Hierarchical Domain Prior CycleGAN: Translated Satellites ISAR Image from the Optical Domain
Huaizhang Liao;Jingyuan Xia;Zhixiong Yang;Zhen Liu;Yongxiang Liu
2023 China Automation Congress (CAC)
Year: 2023 | Conference Paper |
Despite the deep learning has shown revolutionary success in classification tasks, the performance is determined by the training data. Therefore, image augmentation and generation are necessary for the tasks that are lack of sufficient training samples. This paper focuses on generating Inverse Synthetic Aperture Radar (ISAR) images from the optical counterparts, in particular, for the classificati...Show More
A Meta-Learning based Gradient Descent Algorithm for MU-MIMO Beamforming
Zhixiong Yang;Jing-Yuan Xia;Tong Qiu;Huaizhang Liao;Deniz Gündüz
2023 International Conference on Wireless Communications and Signal Processing (WCSP)
Year: 2023 | Conference Paper |
Multi-user multiple-input multiple-output (MU-MIMO) beamforming design is typically formulated as a non-convex weighted sum rate (WSR) maximization problem that is known to be NP-hard. This problem is solved either by iterative algorithms, which suffer from slow convergence, or more recently by using deep learning tools, which require time-consuming pre-training process. In this paper, we propose ...Show More
An Infrared and Optical Image Fusion Method Based on IHS Transformation and a Generative Adversarial Network with Dual Discriminators
Zihao Gao;Changyi Li;Huaizhang Liao;Zhixiong Yang;Jingyuan Xia;Tao Han
2023 5th International Conference on Artificial Intelligence and Computer Applications (ICAICA)
Year: 2023 | Conference Paper |
The image fusion of the optical and infrared images is widely studied in this year to provide better samples for target detection and classification. In this paper, we propose a generative framework for image fusion of the optical and infrared images. This framework is composed by a hierarchical generative adversarial network (GAN) including one generator and two discriminators. Different to the e...Show More
Fast Pre-Training Kernel Estimation Network for ISAR Space Target Image Deblurring
Mingyao Chen;Zhixiong Yang;Huaizhang Liao;Jingyuan Xia;Tianpeng Liu;Li Liu
2023 8th International Conference on Image, Vision and Computing (ICIVC)
Year: 2023 | Conference Paper |
In inverse synthetic aperture radar (ISAR) imaging applications, the lack of image clarity often limits their usage in down-streaming tasks such as recognition and component judgment. The super-resolution of ISAR images after imaging needs to consider the uncertainty of the blur kernel and cannot simply use traditional super-resolution (SR) methods. Moreover, ISAR images are scarce, expensive to o...Show More
Optical-ISAR Image Translation via Denoising Diffusion Implicit Model
Xinyi Wang;Huaizhang Liao;Zhixiong Yang;Tao Han;Jingyuan Xia
2023 IEEE International Conference on Image Processing and Computer Applications (ICIPCA)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Sufficient training data play a crucial role in deep-learning techniques, however, available training samples are not always accessible for specific targets, such as satellites. Producing synthetic data through generative model is a major solution for the lack of training data. In this paper, we propose a Denoising Diffusion Implicit Model (DDIM) to generate the Inverse Synthetic Aperture Radar (I...Show More
Region Detection Based TRA-Net for SAR Target Classification
Xubo Wang;Changyi Li;Zhixiong Yang;Huaizhang Liao;Tao Han;Jingyuan Xia
2023 IEEE International Conference on Image Processing and Computer Applications (ICIPCA)
Year: 2023 | Conference Paper |
In this paper, an attention-mechanism based target region adaptation net (TRA-Net) is designed for synthetic aperture radar (SAR) target recognition and classification. With the fact that SAR image data-acquirement has many obstacles, including poor imaging process and large scale, most of the current deep-learning methods, motivated by convolution neural network or attention mechanism, typically ...Show More
Kernel estimation is an important component in the blind super resolution (BSR), and significantly dominates the performance of the restored image. Most of the state-of-the-art approaches, such as FKP-DIP, is proposed to formulate the kernel estimation as a cumbersome network-based end-to-end model, which provides promising performance through kernel prior pre-training behavior but lack of flexibi...Show More
Representation based Few-Shot Learning for Brand-logo Detection
Zhixiong Yang;Huaizhang Liao;Haoyu Zhang;Weijie Li;Jingyuan Xia
2022 IEEE 4th International Conference on Power, Intelligent Computing and Systems (ICPICS)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
In this paper, we propose an attention-net based few-shot object detection (AN-FSOD) model for brand-logo detection and recognition. With the fact that brand-logo detection has many distinct properties: tiny objects, similar brands, and adversarial images, most of the current FSOD approaches, motivated by meta-learning, metric-learning and transfer learning techniques, typically perform less-effec...Show More
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.