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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zou, Shun Zhang, Zhuo Gao, Guangwei College of Artificial Intelligence Nanjing Agricultural University Nanjing China College of Computer Science and Technology National University of Defense Technology Changsha China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9798350368741

Optical Coherence Tomography Angiography (OCTA) is a crucial imaging technique for visualizing retinal vasculature and diagnosing eye diseases such as diabetic retinopathy and glaucoma. However, precise segmentation of OCTA vasculature remains challenging due to the multi-scale vessel structures and noise from poor image quality and eye lesions. In this study, we proposed OCTAMamba, a novel U-shaped network based on the Mamba architecture, designed to segment vasculature in OCTA accurately. OCTAMamba integrates a Quad Stream Efficient Mining Embedding Module for local feature extraction, a multi-Scale Dilated Asymmetric Convolution Module to capture multi-scale vasculature, and a Focused Feature Recalibration Module to filter noise and highlight target areas. Our method achieves efficient global modeling and local feature extraction while maintaining linear complexity, making it suitable for low-computation medical applications. Extensive experiments on the OCTA 3M, OCTA 6M, and ROSSA datasets demonstrated that OCTAMamba outperforms state-of-the-art methods, providing a new reference for efficient OCTA segmentation. Code is available at https://***/zs1314/OCTAMamba. © 2025 IEEE.

关键词： Mamba OCTA image State Space Model

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An End-to-End Autoencoder-based Probabilistic Shaping Method for Optical Communication 7

An End-to-End Autoencoder-based Probabilistic Shaping Method...

引用

7th Optics Young Scientist Summit, OYSS 2024

作者： Teng, Ziqiang Guo, Dong Gao, Ran Kong, Leyi Han, Wentao Chang, Huan Li, Zhipei Xin, Xiangjun School of Information and Electronics Beijing Institute of Technology 5 Zhongguancun South Street Haidian District Beijing100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology 5 Zhongguancun South Street Haidian District Beijing100081 China Institute of Science and Technology Research Beijing Institute of Technology 5 Zhongguancun South Street Haidian District Beijing100081 China

ISBN: (数字)9781510688377

ISBN: (纸本)9781510688360

The advent of services such as big data and cloud computing has driven a continuous increase in the required transmission rate and capacity of optical fiber communication systems, which now support over 90% of global data traffic. Probability shaping (PS), which adjusts the probability distribution of constellation points, enhances both data transmission rates and the resilience of signals to channel nonlinearity. However, traditional PS methods, such as those using constant composition distribution matching (CCDM), cannot dynamically adjust the probability distribution of constellation points based on channel conditions. To address this limitation, we propose an end-to-end Autoencoder (AE)-based PS technique for optical communication. Simulation results show that compared to traditional 16/64/256QAM, this scheme achieves a maximum improvement of 0.356 bit/symbol in mutual information over the AWGN channel. In the optical fiber channel, under varying signal power and fiber length conditions, the mutual information is improved in different degrees and the influence of channel nonlinearity on the signal is reduced. Innovatively, residual connections are added to the decoder to enhance the model's generalization performance, enabling a deeper network structure and improving its ability to handle complex optical fiber channels. © 2025 SPIE.

关键词： Optical fiber communication

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Object-Aware Video Matting with Cross-Frame Guidance

arXiv

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arXiv 2025年

作者： Zhang, Huayu Wu, Dongyue Shao, Yuanjie Sang, Nong Gao, Changxin National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

Recently, trimap-free methods have drawn increasing attention in human video matting due to their promising performance. Nevertheless, these methods still suffer from the lack of deterministic foreground-background cues, which impairs their ability to consistently identify and locate foreground targets over time and mine fine-grained details. In this paper, we present a trimap-free Object-Aware Video Matting (OAVM) framework, which can perceive different objects, enabling joint recognition of foreground objects and refinement of edge details. Specifically, we propose an Object-Guided Correction and Refinement (OGCR) module, which employs cross-frame guidance to aggregate object-level instance information into pixel-level detail features, thereby promoting their synergy. Furthermore, we design a Sequential Foreground Merging augmentation strategy to diversify sequential scenarios and enhance capacity of the network for object discrimination. Extensive experiments on recent widely used synthetic and real-world benchmarks demonstrate the state-of-the-art performance of our OAVM with only an initial coarse mask. The code and model will be available. © 2025, CC BY.

关键词： Object recognition

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Second Nearest-Neighbor Modified Embedded Atom Method Interatomic Potential for Cu-Ni-Sn Ternary System

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Computers, Materials & Continua 2025年第4期83卷 65-77页

作者： Jialiang Dong Xuemao Dong Zhongxue Feng Caiju Li Jianhong Yi Jun Tan School of Materials Science and Engineering Guizhou Minzu UniversityGuiyang550025China Guangdong Key Laboratory for Advanced Metallic Materials Processing and Forming National Engineering Research Center of Near-Net-Shape Forming for Metallic MaterialsSouth China University of TechnologyGuangzhou510640China Faculty of Electronic and Information Engineering Anshun UniversityAnshun561000China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China College of Materials Science and Engineering Chongqing UniversityChongqing400044China

To explore atomic-level phenomena in the Cu-Ni-Sn alloy,a second nearest-neighbor modified embedded-atom method(2NN MEAM)potential has been developed for the Cu-Ni-Sn system,building upon the work of other *** potential demonstrates remarkable accuracy in predicting the lattice constant,with a relative error of less than 0.5%when compared to density functional theory(DFT)results,and it achieves a 10%relative error in the enthalpy of formation compared to experimental data,marking substantial advancements over prior *** bulk modulus is predicted with a relative error of 8%compared to ***,the potential effectively simulates the processes of melting and solidification of Cu-15Ni-8Sn,with a simulated melting point that closely aligns with the experimental value,within a 7.5%*** serves as a foundation for establishing a 2NN MEAM potential for a flawless Cu-Ni-Sn system and its microalloying systems.

关键词： 2NN MEAM Cu-Ni-Sn interatomic potential atomistic simulation

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Stable Extended U-Net for Noise-Robust Speaker Verification

Stable Extended U-Net for Noise-Robust Speaker Verification

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Wang, Zonghui Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9798350368741

With advancements in deep learning, speaker verification systems have significantly improved their performance in noisy environments. Researchers typically demonstrate the effectiveness of their improved models by comparing performance on specific datasets, such as the VoxCeleb benchmark. However, in diverse real-world noise conditions, the out-of-domain generalization ability is also a crucial factor in evaluating a model's performance improvement. Research on stable learning indicates that eliminating the spurious correlation between training and testing data can enhance the generalization of the model. Building on this idea, we propose an improved speaker verification system with high generalization based on the extended U-Net (ExU-Net). It uses the sample reweighting method from stable learning to eliminate sample correlations and retains more effective speaker information through sub-pixel convolutions and coordinate attention mechanisms. We validate the effectiveness of this approach through extensive evaluations on VoxCeleb1, VOiCES, and other out-of-domain noise test sets, highlighting its generalization capability and model robustness. © 2025 IEEE.

关键词： noisy environments out-of-domain generalization Speaker verification stable learning

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Single foggy image restoration based on spatial correlation analysis of dark channel prior

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Journal of Systems Engineering and Electronics 2014年第4期25卷 688-696页

作者： Yan Tian Dong Xia Yiping Xu National Key Laboratory of Science and Technology on Multi-spectral Information Processing Huazhong University of Science and Technology

Focusing on the degradation of foggy images, a restora- tion approach from a single image based on spatial correlation of dark channel prior is proposed. Firstly, the transmission of each pixel is estimated by the spatial correlation of dark channel prior. Secondly, a degradation model is utilized to restore the foggy image. Thirdly, the final recovered image, with enhanced contrast, is obtained by performing a post-processing technique based on just-noticeable difference. Experimental results demonstrate that the information of a foggy image can be recovered perfectly by the proposed method, even in the case of the abrupt depth changing scene.

关键词： foggy image image restoration dark channel prior,spatial correlation.

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Divide-and-Conquer: Dual-Hierarchical Optimization for Semantic 4D Gaussian Spatting

arXiv

引用

arXiv 2025年

作者： Yan, Zhiying Liang, Yiyuan Cai, Shilv Zhang, Tao Zhong, Sheng Yan, Luxin Zou, Xu Huazhong University of Science and Technology China National Key Laboratory of Multispectral Information Intelligent Processing Technology China Nanyang Technological University Singapore

Semantic 4D Gaussians can be used for reconstructing and understanding dynamic scenes, with temporal variations than static scenes. Directly applying static methods to understand dynamic scenes will fail to capture the temporal features. Few works focus on dynamic scene understanding based on Gaussian Splatting, since once the same update strategy is employed for both dynamic and static parts, regardless of the distinction and interaction between Gaussians, significant artifacts and noise appear. We propose Dual-Hierarchical Optimization (DHO), which consists of Hierarchical Gaussian Flow and Hierarchical Gaussian Guidance in a divide-and-conquer manner. The former implements effective division of static and dynamic rendering and features. The latter helps to mitigate the issue of dynamic foreground rendering distortion in textured complex scenes. Extensive experiments show that our method consistently outperforms the baselines on both synthetic and real-world datasets, and supports various downstream tasks. Project Page: https://***/DHO. Copyright © 2025, The Authors. All rights reserved.

关键词： 3D reconstruction

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Bi-Temporal Feature Relational Distillation for On-Board Lightweight Change Detection in Remote Sensing Imagery

引用

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wang, Guoqing Chen, He Li, Jie Wang, Jue Liu, Wenchao Chen, Liang Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China Shanghai Aerospace Electronic Technology Institute Shanghai201109 China

Deep learning models for remote sensing change detection (CD) require significant resources, challenging real-time applications on spaceborne devices. Knowledge distillation (KD) technology is a potential solution that balances model size and CD performance. However, existing KD methods struggle to effectively transfer the teacher's ability to respond to changed landcovers, and also face challenges in assisting the student model in detecting changes in landcovers with subtle visual characteristics. To overcome these limitations, a bi-temporal feature relational distillation (FRD) framework is proposed. The FRD framework includes two designed distillation components: bi-temporal feature distance distillation (BFDD) and contrastive cluster representation distillation (CCRD). BFDD guides the student model to learn the relationship between bi-temporal feature categories from the teacher model and introduces the iteration-wise random selection and bilinear interpolation strategy to solve feature mismatch problem between the teacher and the student. CCRD describes the pixel-level and clusterlevel feature distributions through the designed contrastive cluster (CC) module, guiding the student model to align the change/nonchange features with the teacher model, so as to alleviate the confusion of change/non-change features. Extensive experiments and analyses have confirmed that the proposed FRD framework can develop lightweight models capable of achieving performance comparable to large models, thereby enhancing suitability for realtime CD. © 1965-2011 IEEE.

关键词： Change detection

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Learning to Deblur Polarized Images

引用

International Journal of Computer Vision 2025年 1-16页

作者： Zhou, Chu Teng, Minggui Zhou, Xinyu Xu, Chao Sato, Imari Shi, Boxin National Institute of Informatics Tokyo Japan National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China State Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China National Key Laboratory of General AI School of Intelligence Science and Technology Peking University Beijing China

A polarization camera can capture four linear polarized images with different polarizer angles in a single shot, which is useful in polarization-based vision applications since the degree of linear polarization (DoLP) and the angle of linear polarization (AoLP) can be directly computed from the captured polarized images. However, since the on-chip micro-polarizers block part of the light so that the sensor often requires a longer exposure time, the captured polarized images are prone to motion blur caused by camera shakes, leading to noticeable degradation in the computed DoLP and AoLP. Deblurring methods for conventional images often show degraded performance when handling the polarized images since they only focus on deblurring without considering the polarization constraints. In this paper, we propose a polarized image deblurring pipeline to solve the problem in a polarization-aware manner by adopting a divide-and-conquer strategy to explicitly decompose the problem into two less ill-posed sub-problems, and design a two-stage neural network to handle the two sub-problems respectively. Experimental results show that our method achieves state-of-the-art performance on both synthetic and real-world images, and can improve the performance of polarization-based vision applications such as image dehazing and reflection removal. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep learning Image deblurring Polarization camera Polarized images

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Covert Communications in D2D Underlaying Cellular Networks With Dual Uncertainties at the Warden

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IEEE Internet of Things Journal 2025年

作者： Du, Zhiyi Chen, Weiyu Luo, Junshan Ding, Haiyang Wang, Shilian Sun, Zhengbo National University of Defense Technology College of Electronic Science and Technology Changsha410073 China National University of Defense Technology College of Information and Communication Wuhan430019 China National Key Laboratory on Blind Signal Processing Chengdu610041 China

Covert device-to-device (D2D) communication aims to hide the existence of D2D transmission behavior from the warden, which plays a pivotal role in bolstering D2D communication security. The existing research on covert D2D communication either considered the interference's assistance with perfect channel state information (CSI) available at the warden, or considered channel distribution information (CDI) at the warden without the assistance of interference. These setups facilitate the performance analyses and the parameters design, which, however, did not fully exploit the potential of covert D2D communication, since both channel uncertainty and interference uncertainty exist in practice. In this paper, two covert schemes are proposed for covert D2D communication with dual uncertainties at the warden, where the warden has imperfect CSI. The base station transmits artificial noise to assist covert communication in the first scheme. In the second scheme, the cellular user adopts variable power to confuse the warden. For both schemes, the closed-form expressions of the warden's approximate minimum detection error probability and the achievable D2D communication rate are derived. The achievable D2D communication rate is maximized by jointly optimizing the transmit power of different links with given covertness requirement. The results suggest that the proposed schemes achieve higher communication performance by considering dual uncertainties. Moreover, the scheme with variable cellular user's transmit power performs better when the covert receiver can not well handle the interference from artificial noise. © 2025 IEEE.

关键词： Mobile telecommunication systems

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