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Watch You under Low-Resolution and Low-Illumination: Face Enhancement via Bi-Factor Degradation Decoupling

IEEE Transactions on Circuits and Systems for Video Technology 2024年第5期34卷 3481-3495页

作者： Ding, Xin Wang, Zheng Fang, Jing Shu, Zhenyu Hu, Ruimin Lin, Chia-Wen NingboTech University School of Computer and Data Engineering Ningbo315100 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Wuhan430072 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu300044 Taiwan

Face enhancement aims to improve low-quality face images to a higher-quality level. However, in real-world nighttime scenes, complex degradation factors often affect these images, making it challenging to preserve important facial details. Existing image enhancement algorithms typically focus on independently conducting image super-resolution and brightness enhancement, assuming a fixed degradation level based on simulated training datasets. Nonetheless, real nighttime scenes involve complex degradation processes, where degradation factors dynamically and variably manifest. Therefore, achieving effective face enhancement in such scenarios is particularly daunting. This work analyzes and unveils the multiple factors of low resolution and low illumination during degradation. Based on this analysis, we propose a Bi-factor Degradation Decoupling network. Our method leverages a decoupling network to generate qualitative and quantitative features corresponding to each factor's degradation degree in the low-quality environment. These features are then combined with robust facial feature constraints to recover the details of low-quality faces. Extensive experiments demonstrate that our method surpasses state-of-the-art approaches in both enhancement and face super-resolution. © 1991-2012 IEEE.

关键词： Image reconstruction

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A Tutorial on Federated Learning from Theory to Practice:Foundations,software Frameworks,Exemplary Use Cases,and Selected Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information Science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： data privacy distributed machine learning federated learning software frameworks

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Design of PMCW Millimeter Wave Radar Algorithms on an Embedded DSP 16

Design of PMCW Millimeter Wave Radar Algorithms on an Embedd...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Zhao, Jinghao Liu, Qiuming Tan, Bin School of Software engineering Jiangxi University of Science and Technology Nanchang China Nanchang Key laboratory of Virtual Digital Factory and Cultural Communications Nanchang China Jiangxi Provincial Key Laboratory of Electronic Data Control and Forensics China School of Electronics and Information Engineering Jinggangshan University Ji’an China

ISBN: (纸本)9781510689251

The rapid advancements in autonomous driving technology necessitate the extensive deployment of automotive radars operating within the 77-81 GHz millimeter-wave band in the forthcoming years. In contrast to earlier Frequency Modulated Continuous Wave (FMCW) radars, Phase-Modulated Continuous Wave (PMCW) radars exhibit notable improvements in processing speed and flexibility, offering superior range and velocity resolution. These enhancements are critical for the precise detection and interpretation of dynamic and complex traffic scenarios. This paper initially presents the simulation and testing of algorithms for range and speed measurement using single-input single-output (SISO) PMCW millimeter-wave radar. Building upon these results, further simulations incorporating multi-input multi-output (MIMO) systems utilizing Hadamard codes are conducted to augment PMCW radar performance. The final phase of this study involves implementing the relevant signal processing algorithms on a custom-developed digital signal processor (DSP) named SWIFT. Experimental findings demonstrate that PMCW radar significantly mitigates multipath effects and clutter, maintaining high performance in complex environments. Furthermore, the algorithms executed on the DSP meet the anticipated performance standards. The proposed methodology not only validates the theoretical framework but also establishes a foundation for future hardware implementation. © 2025 SPIE.

关键词： Automotive radar

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Gray-scale transformation for traffic classification: leveraging local and global features in flow data 3

Gray-scale transformation for traffic classification: levera...

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3rd International Conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Xia, Yuchao Xue, Mutian Li, Wei Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China University of Electronic Science and Technology of China Chengdu China Sichuan University of Media and Communications Chengdu China

ISBN: (纸本)9781510680449

The rapid expansion and diversification of internet traffic pose a pressing demand for accurate classification of diverse application streams in network management and security monitoring. This paper introduces a deep learning-based method for precise classification of diverse internet application streams in network. By transforming raw traffic data into grayscale images and utilizing Convolutional Neural Networks (CNNs) and the Swin-Transformer framework, critical information is extracted at both local and global feature levels. The core of this method is offering a visual representation of the structure and content of traffic data. This visual representation enables subsequent feature extraction and classification. Through experimentation across various applications including email, Skype, YouTube, Vimeo, FTP, and Facebook, the proposed method in traffic classification is demonstrated to be effective. This approach boosts traffic classification accuracy, offers innovation in network management and security monitoring, and holds vital importance in optimizing network performance and security. Additionally, it sets the stage for future research in networking data processing and security methodologies. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Network security

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XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

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Science China(Information Sciences) 2024年第7期67卷 46-71页

作者： Xiaolong XU Fan WU Muhammad BILAL Xiaoyu XIA Wanchun DOU Lina YAO Weiyi ZHONG School of Software Nanjing University of Information Science and Technology School of Computing and Communications Lancaster University School of Computing Technologies Royal Melbourne Institute of Technology State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering University of New South Wales Data 61 Commonwealth Scientific and Industrial Research Organization School of Computer Science Qufu Normal University

Content delivery networks(CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning(DRL) offers a promising solution for intelligent zero-touch network governance. However, the blackbox nature of DRL models poses challenges in understanding and making trusting decisions. In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence(XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent's Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental *** results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service(QoS),a

关键词： deep reinforcement learning (DRL) explainable artificial intelligence (XAI) multi-level cache content delivery network (CDN) D3QN algorithm Deep-SHAP

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

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

作者： Li, Xin Xu, Feng Tao, Feifei Tong, Yao Lyu, Xin Zhong, Jianyi Kaup, André College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9798350368741

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study. © 2025 IEEE.

关键词： frequency transformer remote sensing images Semantic segmentation spectrum enhancement

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TOWARDS FAITHFUL XAI EVALUATION VIA GENERALIZATION-LIMITED BACKDOOR WATERMARK 12

TOWARDS FAITHFUL XAI EVALUATION VIA GENERALIZATION-LIMITED B...

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12th International Conference on Learning Representations, ICLR 2024

作者： Ya, Mengxi Li, Yiming Dai, Tao Wang, Bin Jiang, Yong Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China The State Key Laboratory of Blockchain and Data Security Zhejiang University China ZJU-Hangzhou Global Scientific and Technological Innovation Center China College of Computer Science and Software Engineering Shenzhen University China Guangzhou Intelligence Communications Technology Co. Ltd. China

Saliency-based representation visualization (SRV) (e.g., Grad-CAM) is one of the most classical and widely adopted explainable artificial intelligence (XAI) methods for its simplicity and efficiency. It can be used to interpret deep neural networks by locating saliency areas contributing the most to their predictions. However, it is difficult to automatically measure and evaluate the performance of SRV methods due to the lack of ground-truth salience areas of samples. In this paper, we revisit the backdoor-based SRV evaluation, which is currently the only feasible method to alleviate the previous problem. We first reveal its implementation limitations and unreliable nature due to the trigger generalization of existing backdoor watermarks. Given these findings, we propose a generalization-limited backdoor watermark (GLBW), based on which we design a more faithful XAI evaluation. Specifically, we formulate the training of watermarked DNNs as a min-max problem, where we find the 'worst' potential trigger (with the highest attack effectiveness and differences from the ground-truth trigger) via inner maximization and minimize its effects and the loss over benign and poisoned samples via outer minimization in each iteration. In particular, we design an adaptive optimization method to find desired potential triggers in each inner maximization. Extensive experiments on benchmark datasets are conducted, verifying the effectiveness of our generalization-limited watermark. Our codes are available at https://***/yamengxi/GLBW. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Deep neural networks

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An Optimal Network Coverage-oriented Operators Network Automatic Planning Strategy 5

An Optimal Network Coverage-oriented Operators Network Autom...

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5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Gou, Haosong Su, Ling Zhang, Gaoyi Huang, Wei Yang, Yang Rao, Yunbo China Mobile Communications Group Sichuan Co. Ltd. Chengdu China Chengdu Aerospace Science and Industry Big Data Research Institute Co. Ltd. Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

With the constant expansion of the scale of the operators' network, the difficulty of network planning is also significantly increased. The traditional way of manual network planning highly depends on engineers' experience, which results in many quality and efficiency problems, especially in the issues of coverage holes in the existing network, which usually requires a lot of human resources for the network planning. This paper proposes an optimal network coverage-oriented operators' network automatic planning strategy, the mathematical model can quickly deduce the optimal network topology and base station location. The final experimental analysis shows that our proposed method achieves much better performance on automatic network planning through a mathematical model than manually network planning. It saves lots of human resources in network planning and improves the efficiency and quality of network planning. © 2022 IEEE.

关键词： Personnel

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Antenna Pattern Design in Frequency Selective Channels

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IEEE Wireless communications Letters 2025年

作者： Zhang, Cuicui Wang, Shuo Chen, Xin Zhang, Ming Wang, Kewei Zheng, Tong-Xing Zhang, Anxue Xi’an Jiaotong University School of Information and Communications Engineering Shanxi Xi’an710049 China Autonavi Software Co. Ltd Beijing100102 China Purple Mountain Laboratories Nanjing211111 China National Key Laboratory of Multi-domain Data Collaborative Processing and Control Xi’an710068 China

Antenna configuration plays a critical role in improving channel capacity through optimized antenna spacing, radiation patterns, and coupling. While previous works have explored antenna pattern design for flat fading channels, the optimization of patterns for frequency selective channels, remains under-explored. This paper addresses the antenna pattern design aimed at maximizing capacity in frequency selective channels. We propose a novel discrete representation model for antenna patterns. Based on that, the capacity maximization problem is formulated and solved using a combination of alternating optimization, convex optimization, auxiliary variable method, and the bisection method to achieve antenna pattern design. Numerical simulations validate the effectiveness of the proposed approach, demonstrating significant improvements in system capacity. © 2012 IEEE.

关键词： Directional patterns (antenna)

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Irregularly sampled seismic data interpolation via wavelet-based convolutional block attention deep learning

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Artificial Intelligence in Geosciences 2022年第1期3卷 192-202页

作者： Yihuai Lou Lukun Wu Lin Liu Kai Yu Naihao Liu Zhiguo Wang Wei Wang Center for Hypergravity Experimental and Interdisciplinary Research Zhejiang UniversityHangzhouZhejiang 310058China MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhouZhejiang 310058China School of Software Engineering Xi’an Jiaotong UniversityXi’anShaanxi 710049China Petrochina CBM Company Limited Beijing 100028China Capiticalonline Data Service Co. LTDBeijing 100012China School of Information and Communications Engineering Xi’an Jiaotong UniversityXi’anShaanxi 710049China School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’anShaanxi 710049China PowerChina Huadong Engineering Corporation Limited HangzhouZhejiang 310014China

Seismic data interpolation,especially irregularly sampled data interpolation,is a critical task for seismic processing and subsequent ***,with the development of machine learning and deep learning,convolutional neural networks(CNNs)are applied for interpolating irregularly sampled seismic *** based approaches can address the apparent defects of traditional interpolation methods,such as the low computational efficiency and the difficulty on parameters ***,current CNN based methods only consider the temporal and spatial features of irregularly sampled seismic data,which fail to consider the frequency features of seismic data,i.e.,the multi-scale *** overcome these drawbacks,we propose a wavelet-based convolutional block attention deep learning(W-CBADL)network for irregularly sampled seismic data *** firstly introduce the discrete wavelet transform(DWT)and the inverse wavelet transform(IWT)to the commonly used U-Net by considering the multi-scale features of irregularly sampled seismic ***,we propose to adopt the convolutional block attention module(CBAM)to precisely restore sampled seismic traces,which could apply the attention to both channel and spatial ***,we adopt the proposed W-CBADL model to synthetic and pre-stack field data to evaluate its validity and *** results demonstrate that the proposed W-CBADL model could reconstruct irregularly sampled seismic data more effectively and more efficiently than the state-of-the-art contrastive CNN based models.

关键词： Irregularly sampled seismic data reconstruction Deep learning U-Net Discrete wavelet transform Convolutional block attention module

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