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Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne Massive MIMO Network

ieee JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2025年第1期43卷 260-278页

作者： Wu, Minghui Gao, Zhen Wang, Zhaocheng Niyato, Dusit Karagiannidis, George K. Chen, Sheng Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol BIT Zhuhai 519088 Peoples R China BIT State Key Lab CNS ATM Beijing 100081 Peoples R China BIT MIIT Key Lab Complex Field Intelligent Sensing Beijing 100081 Peoples R China BIT Jinan Adv Technol Res Inst Jinan 250307 Peoples R China BIT Jiaxing Yangtze Delta Reg Acad Jiaxing 314019 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki 54124 Greece Lebanese Amer Univ LAU Artificial Intelligence & Cyber Syst Res Ctr Beirut 03797751 Lebanon Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England

Near-space airship-borne communication network is recognized to be an indispensable component of the future integrated ground-air-space network thanks to airships' advantage of long-term residency at stratospheric altitudes, but it urgently needs reliable and efficient Airship-to-X link. To improve the transmission efficiency and capacity, this paper proposes to integrate semantic communication with massive multiple-input multiple-output (MIMO) technology. Specifically, we propose a deep joint semantic coding and beamforming (JSCBF) scheme for airship-based massive MIMO image transmission network in space, in which semantics from both source and channel are fused to jointly design the semantic coding and physical layer beamforming. First, we design two semantic extraction networks to extract semantics from image source and channel state information, respectively. Then, we propose a semantic fusion network that can fuse these semantics into complex-valued semantic features for subsequent physical-layer transmission. To efficiently transmit the fused semantic features at the physical layer, we then propose the hybrid data and model-driven semantic-aware beamforming networks. At the receiver, a semantic decoding network is designed to reconstruct the transmitted images. Finally, we perform end-to-end deep learning to jointly train all the modules, using the image reconstruction quality at the receivers as a metric. The proposed deep JSCBF scheme fully combines the efficient source compressibility and robust error correction capability of semantic communication with the high spectral efficiency of massive MIMO, achieving a significant performance improvement over existing approaches.

关键词： Semantics Array signal processing Massive MIMO Communication systems image reconstruction Iterative decoding Decoding Airship base station beamforming massive MIMO deep learning semantic communication

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Viewport-Independent Blind Quality Assessment of AI-Generated Omnidirectional images via Vision-Language Correspondence

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ieee signal processing LETTERS 2025年 32卷 1630-1634页

作者： Liu, Xuelin Yan, Jiebin Lai, Chenyi Li, Yang Fang, Yuming Jiangxi Univ Finance & Econ Sch Comp & Artificial Intelligence Nanchang 330032 Peoples R China

The advancement of deep generation technology has significantly enhanced the growth of artificial intelligence-generated content (AIGC). Among these, AI-generated omnidirectional images (AGOIs), hold considerable promise for applications in virtual reality (VR). However, the quality of AGOIs varies widely, and there has been limited research focused on their quality assessment. In this letter, inspired by the characteristics of the human visual system, we propose a novel viewport-independent blindquality assessment method for AGOIs, termed VI-AGOIQA, which leverages vision-language correspondence. Specifically, to minimize the computational burden associated with viewport-based prediction methods for omnidirectional image quality assessment, a set of image patches are first extracted from AGOIs in Equirectangular Projection (ERP) format. Then, the correspondence between visual and textual inputs is effectively learned by utilizing the pre-trained image and text encoders of the Contrastive Language-image Pre-training (CLIP) model. Finally, a multimodal feature fusion module is applied to predict human visual preferences based on the learned knowledge of visual-language consistency. Extensive experiments conducted on publicly available database demonstrate the promising performance of the proposed method.

关键词： Visualization Feature extraction Databases Quality assessment Predictive models Training computational modeling Vectors Solid modeling image quality AI-generated omnidirectional image vision-language correspondence blind image quality assessment

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Variational Bayes image Restoration With Compressive Autoencoders

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ieee transactions on image processing : a publication of the ieee signal processing Society 2025年 PP卷 2896-2909页

作者： Maud Biquard Marie Chabert Florence Genin Christophe Latry Thomas Oberlin Fédération ENAC ISAE-SUPAERO ONERA Université de Toulouse Toulouse France IRIT and Toulouse INP Université de Toulouse Toulouse France Centre National d’Études Spatiales (CNES) Toulouse France

Regularization of inverse problems is of paramount importance in computational imaging. The ability of neural networks to learn efficient image representations has been recently exploited to design powerful data-driven regularizers. While state-of-the-art plug-and-play (PnP) methods rely on an implicit regularization provided by neural denoisers, alternative Bayesian approaches consider Maximum A Posteriori (MAP) estimation in the latent space of a generative model, thus with an explicit regularization. However, state-of-the-art deep generative models require a huge amount of training data compared to denoisers. Besides, their complexity hampers the optimization involved in latent MAP derivation. In this work, we first propose to use compressive autoencoders instead. These networks, which can be seen as variational autoencoders with a flexible latent prior, are smaller and easier to train than state-of-the-art generative models. As a second contribution, we introduce the Variational Bayes Latent Estimation (VBLE) algorithm, which performs latent estimation within the framework of variational inference. Thanks to a simple yet efficient parameterization of the variational posterior, VBLE allows for fast and easy (approximate) posterior sampling. Experimental results on image datasets BSD and FFHQ demonstrate that VBLE reaches similar performance as state-of-the-art PnP methods, while being able to quantify uncertainties significantly faster than other existing posterior sampling techniques. The code associated to this paper is available in https://***/MaudBqrd/VBLE.

关键词： Inverse problems Autoencoders image coding computational modeling Training Bayes methods Quantization (signal) Estimation Noise measurement Manifolds

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ieee SSCI 2011 - symposium Series on computational intelligence - CIMSIVP 2011: 2011 ieee symposium on computational intelligence for Multimedia, signal and Vision processing

IEEE SSCI 2011 - Symposium Series on Computational Intellige...

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symposium Series on computational intelligence, ieee SSCI 2011 - 2011 ieee symposium on computational intelligence for Multimedia, signal and Vision processing, CIMSIVP 2011

ISBN: (纸本)9781424499151

The proceedings contain 20 papers. The topics discussed include: a 9/7 wavelet-based lossless data hiding;computational visual attention model capable of exploring similarity;extracting salient lines by visual attention for omnidirectional image classification;exploiting automatic image segmentation to human detection and depth estimation;local contrast stretch based tone mapping for high dynamic range images;environmental sound extraction and incremental learning approach for real time concepts identification;context-based image re-ranking for content-based image retrieval;block motion estimation based on search pattern and predictor;dynamic vision sensor camera based bare hand gesture recognition;multimodal fuzzy fusion system for semantic video indexing;new frame rate up-conversion based on foreground/background segmentation;and semantic concepts categorization based on pseudo-sentences representation.

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MDN: Mamba-Driven Dualstream Network For Medical Hyperspectral image Segmentation

MDN: Mamba-Driven Dualstream Network For Medical Hyperspectr...

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2025 ieee International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Lin, Shijie Yun, Boxiang Shen, Wei Li, Qingli Yang, Anqiang Wang, Yan Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai China Department of Pathology Changning Maternal and Infant Health Hospital Shanghai China

ISBN: (纸本)9798350368741

Medical Hyperspectral Imaging (MHSI) offers potential for computational pathology and precision medicine. However, existing CNN and Transformer struggle to balance segmentation accuracy and speed due to high spatial-spectral dimensionality. In this study, we leverage Mamba's global context modeling to propose a dual-stream architecture for joint spatial-spectral feature extraction. To address the limitation of Mamba's unidirectional aggregation, we introduce a recurrent spectral sequence representation to capture low-redundancy global spectral features. Experiments on a public Multi-Dimensional Choledoch dataset and a private Cervical Cancer dataset show that our method outperforms state-of-the-art approaches in segmentation accuracy while minimizing resource usage and achieving the fastest inference speed. Our code will be available at https://***/DeepMed-Lab-ECNU/MDN. © 2025 ieee.

关键词： dualstream Mamba Medical hyperspectral image segmentation

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DARNet: A Dual Attention Residual Network for Medical image Classification

DARNet: A Dual Attention Residual Network for Medical Image ...

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2025 ieee International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Ao Guan, Zhenghua Zhang, Tengda Hu, Wenzheng Liu, Yi Lei, Baiying School of Biomedical Engineering Shenzhen University Shenzhen China Artificial Intelligence Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

In the field of medical image analysis, accurate classification of images is crucial for diagnosing diseases and formulating treatment plans. Many studies have shown that global features and local features help reduce noise interference in medical images. Due to the fixed receptive field size of the convolution kernel, it is difficult to capture the global features of the image. Although the self-attention-based Transformer can model long-range dependencies, it has high computational complexity and lacks local inductive bias. This paper proposes a new module based on dual attention, the Dual Attention Residual Module(DARNet), which uses multi-head self-attention (MHSA) to enhance the global feature extraction ability of convolutional neural network (CNN), while the convolutional block attention module (CBAM) enhances local feature extraction, and then fuses features at different levels through dual-step attention fusion (DSAF). Then we use the architecture of resnet to build the network-Dual Attention Residual Network (DARNet). We evaluate our network on the ISIC2018 and Kvasir datasets and demonstrate its superior performance compared with state-of-the-art models. © 2025 ieee.

关键词： endoscopic gastrointestinal diseases feature fusion medical image classification skin cancer

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ieee SSCI 2014 - 2014 ieee symposium Series on computational intelligence - CIMSIVP 2014: 2014 ieee symposium on computational intelligence for Multimedia, signal and Vision processing, Proceedings

IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational...

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2014 ieee symposium on computational intelligence for Multimedia, signal and Vision processing, CIMSIVP 2014

ISBN: (纸本)9781479945047

The proceedings contain 34 papers. The topics discussed include: stereoscopic video description for human action recognition;cascade dictionary learning for action recognition;human action recognition using normalized cone histogram features;fuzzy rules based indoor human action recognition using multi cameras;improving codebook generation for action recognition using a mixture of asymmetric Gaussians;endoscope image analysis method for evaluating the extent of early gastric cancer;fuzzy C-means clustering with spatially weighted information for medical image segmentation;improve recognition performance by hybridizing principal component analysis (PCA) and elastic bunch graph matching (EBGM);and automatic tumor lesion detection and segmentation using histogram-based gravitational optimization algorithm.

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Proceedings of the 2013 ieee symposium on computational intelligence for Multimedia, signal and Vision processing, CIMSIVP 2013 - 2013 ieee symposium Series on computational intelligence, SSCI 2013

Proceedings of the 2013 IEEE Symposium on Computational Inte...

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2013 ieee symposium on computational intelligence for Multimedia, signal and Vision processing, CIMSIVP 2013 - 2013 ieee symposium Series on computational intelligence, SSCI 2013

ISBN: (纸本)9781467359177

The proceedings contain 8 papers. The topics discussed include: efficient determination of spatial relations using composition tables and decision trees;smooth transition neighborhood graphs for 3D spatial relations;biomimetic target detection: modeling 2nd order correlation of OFF and ON channels;a novel algorithm for detecting dim point target in infrared images based on MRF;robust gesture detection and recognition using dynamic time warping and multi-class probability estimates;regularized high temporal resolution fish motion synthesis;using fuzzy rough feature selection for image retrieval system;and forward-backward minima controlled recursive averaging to speech enhancement.

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SFma-Unet: A Mamba-Based Spatial-Frequency Fusion Network for Medical image Segmentation

SFma-Unet: A Mamba-Based Spatial-Frequency Fusion Network fo...

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2025 ieee International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Liu, Zhanpeng Zhang, Yuqiang Wang, Bin Yang, Yang Cai, Lin Institute of Artificial Intelligence Xiamen University Xiamen China School of Artificial Intelligence Hubei University Wuhan China School of Life Sciences Hubei University Wuhan China

ISBN: (纸本)9798350368741

Recently, Mamba-based methods have gained popularity in medical image segmentation due to their ability to model long-range dependencies with linear computational complexity. However, current segmentation methods often face challenges such as low contrast, blurred boundaries, and unclear backgrounds in medical images. Considering that perceived objects and features exhibit greater discriminative power in the frequency domain, we propose a novel mamba-based spatial and frequency domain feature fusion network, SFMa-Unet, to address these challenges. Specifically, we designed the Spatial-Frequency Interaction (SFI) module, which leverages the powerful modeling capabilities of Mamba to fuse spatial and frequency domain features, enhancing feature representation. Additionally, we developed a Mamba-based multi-scale feature channel fusion (MFCF) bridge to capture local and global dependencies across different feature scales, further improving the model's representational capacity. We conduct comprehensive experiments on the ISIC17 and ISIC18 public datasets. Experimental results demonstrate the effectiveness and robustness of SFMa-Unet. Codes are available at https://***/RainCh-zyq/SFma-Unet. © 2025 ieee.

关键词： Medical image Segmentation spatial-frequency fusion Vision State Space Models

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Adaptive Multi-Scale Local Correction for Semi-Supervised 3D Medical image Segmentation

Adaptive Multi-Scale Local Correction for Semi-Supervised 3D...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Xinqiang Wang Wenhuan Lu Ke Zheng Junhai Xu College of Intelligence and Computing Tianjin University Tianjin China School of Software and Communication Tianjin Sino-German University of Applied Sciences Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, semi-supervised 3D medical image segmentation has gained significant attention. However, current methods often struggle with multi-scale voxel differences and overlook the importance of loss weight balancing. To address these issues, we propose an adaptive multi-scale local correction method (AMLC). Our key contributions are: (1) a multi-scale local correction module to accurately capture differences between 3D voxel blocks at various scales; (2) an adaptive weighting adjustment module that dynamically balances losses, improving robustness and training. Experiments show that AMLC significantly outperforms existing methods on the public dataset, demonstrating its effectiveness.

关键词： Training image segmentation Adaptation models Solid modeling Three-dimensional displays computational modeling signal processing Robustness Speech processing Biomedical imaging

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