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Variational Weighted p−q Regularization for Hyperspectral Image Restoration Under Mixed Noise

IET Image Processing 2025年第1期19卷

作者： Aetesam, Hazique Prasath, V. B. Surya Department of Computer Science and Engineering Birla Institute of Technology Mesra Bihar Patna India Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center CincinnatiOH United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH United States Department of Biomedical Informatics University of Cincinnati CincinnatiOH United States Department of Computer Science University of Cincinnati CincinnatiOH United States

In this paper, we propose to use weighted (Formula presented.) -norm for approximating the solution of general (Formula presented.) -norm regularization problem for recovering hyperspectral images (HSI) corrupted by a mixture of Gaussian-impulse noise. As a special case of (Formula presented.), we design an optimization framework to accommodate the combined effect of different noise sources. An initial impulse noise pre-detection phase decouples the raw noisy HSI data into impulse and Gaussian corrupted pixels. Gaussian corrupted pixels are handled by data-fidelity term in (Formula presented.) while impulse corrupted pixels possess more Laplacian like behavior;modeled using (Formula presented.). Solutions of problems involving (Formula presented.) in data fidelity and regularization terms complicate the optimization process but are less sensitive to the outlier pixels. On the other hand, the least square solutions for the data misfit are computationally efficient but generates solutions which are quite sensitive to the outlier pixels;which is the characteristic of impulse corrupted pixels. Therefore, in this paper, we decouple the set of pixels into two distinct parts;handled using two separate data fidelity terms. Total variation (TV) is used on the Casorati matrix representation of the input data to exploit similarity along both spatial and spectral dimensions. The resulting optimization problem is reformulated as iteratively reweighted least square for the general (Formula presented.) -norm problem for (Formula presented.) for data fidelity terms and (Formula presented.) for the TV regularization term. Experiments conducted over synthetically corrupted HSI data and images obtained from real HSI sensors confirm the suitability of the proposed weighted norm optimization framework (WNOF) over a wide range of degradation scenarios. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Image denoising

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A Dynamic Optimization Framework for Computation Rate Maximization in UAV-Assisted Mobile Edge Computing

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IEEE Transactions on Vehicular Technology 2025年

作者： Chen, Yang Pi, Dechang Yang, Shengxiang Xu, Yue Wang, Bi Qin, Shuo Wang, Yintong Xi'an University of Science and Technology Artificial Intelligence and Computer Science Xi'an China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China De Montfort University School of Computer Science and Informatics LeicesterLE1 9BH United Kingdom Jiangxi University of Science and Technology Faculty of Information Engineering Ganzhou China Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an China Nanjing Xiaozhuang University School of Information Engineering Nanjing China

Mobile edge computing (MEC) significantly boosts the computing power and reduces the energy consumption of Internet of Things (IoT) devices, serving as a valuable complement to cloud computing. The application of unmanned aerial vehicle (UAV) for MEC systems can effectively alleviate the issue of insufficient or damaged communication facilities in remote areas, further expanding the scope of MEC applications. In this article, we present a system model for UAV-assisted wireless-powered MEC systems in a dynamic environment with the objective of maximizing the computation rate of user devices. Due to the complexity of the optimization objective in dynamic environments, we propose a swarm intelligence-based optimization framework with a mechanism for responding to environmental changes, which is intended to enhance population diversity in both static and dynamic environments with the aim of overcoming premature convergence. We integrate particle swarm optimization and harmony search into the proposed framework, naming them DOPSO and DOHS, respectively. Simulation results for two offloading modes in UAV-assisted MEC systems indicate that the proposed framework significantly outperforms other dynamic optimization algorithms. © 1967-2012 IEEE.

关键词： Energy utilization

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DSLR-CNN: Efficient CNN Acceleration using Digit-Serial Left-to-Right Arithmetic

arXiv

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arXiv 2025年

作者： Nisar, Malik Zohaib Ibrahim, Muhammad Sohail Gorgin, Saeid Usman, Muhammad Lee, Jeong-A Department of Computer Engineering College of IT Convergence Chosun University Gwangju61452 Korea Republic of Faculty of Informatics and Data Science University of Regensburg Regensburg93053 Germany Tehran33535-111 Iran

Digit-serial arithmetic has emerged as a viable approach for designing hardware accelerators, reducing interconnections, area utilization, and power consumption. However, conventional methods suffer from performance and latency issues. To address these challenges, we propose an accelerator design using left-to-right (LR) arithmetic, which performs computations in a most-significant digit first (MSDF) manner, enabling digit-level pipelining. This leads to substantial performance improvements and reduced latency. The processing engine is designed for convolutional neural networks (CNNs), which includes low-latency LR multipliers and adders for digit-level parallelism. The proposed DSLR-CNN is implemented in Verilog and synthesized with Synopsys design compiler using GSCL 45nm technology, the DSLR-CNN accelerator was evaluated on AlexNet, VGG-16, and ResNet-18 networks. Results show significant improvements across key performance evaluation metrics, including response time, peak performance, power consumption, operational intensity, area efficiency, and energy efficiency. The peak performance measured in GOPS of the proposed design is 4.37× to 569.11× higher than contemporary designs, and it achieved 3.58× to 44.75× higher peak energy efficiency (TOPS/W), outperforming conventional bit-serial designs. © 2025, CC BY.

关键词： Convolutional neural networks

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Editorial – Elsevier smart health special issue: Advancing ICT for health, accessibility, and wellbeing

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Smart Health 2025年 36卷

作者： Achilleos, Achilleas Pissaloux, Edwige Papadopoulos, George A. Velazquez, Ramiro Faculty of Engineering Universidad Panamericana Mexico Department of Computer Science University of Cyprus 75 Kallipoleos St P.O Box 20537 Nicosia CY 1678 Cyprus Physics Department University of Rouen France Department of Electrical Engineering Computer Engineering and Informatics Frederick University Cyprus

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LLM Assistance for Pediatric Depression

arXiv

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arXiv 2025年

作者： Ignashina, Mariia Bondaronek, Paulina Santel, Dan Pestian, John Ive, Julia University College London Institute of Health Informatics United Kingdom Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center University of Cincinnati CincinnatiOH United States

Introduction: Depression is a complex mental health condition, particularly prevalent among young people aged 10–24, a group experiencing a sharp rise in cases. Traditional screening methods, such as the PHQ-9, are particularly challenging for children in pediatric primary care due to practical limitations. AI has potential to help but the scarcity of annotated datasets in mental health, combined with the computational costs of training, highlights the need for efficient, zero-shot approaches. Large Language Models (LLMs) offer a promising, computationally affordable zero-shot solution by extracting relevant text segments from electronic patient notes to support clinicians in identifying depressive symptoms, as well as for downstream AI. In this work, we investigate the feasibility of state-of-the-art LLMs for depressive symptom extraction in pediatric settings (ages 6–24). This approach aims to complement traditional screening and minimize diagnostic errors. Methods: We examined free text of the EHRs of pediatric patients with the diagnosis of depression or related mood disorders (age groups 6-24, 1.8K patients) from at the Cincinnati Children’s Hospital Medical Center. We noticed drastic inconsistencies in the application and documentation of PHQ-9 screening highlighting the difficulty in obtaining comprehensive diagnostic data in these conditions. We manually annotated notes for 22 patients with 16 depression-related symptom categories. We leveraged the combination of Beck’s Depression Inventory (BDI) and the PHQ-9 to develop tailored categories specifically suited for pediatric depression symptoms. We then applied three state-of-the-art Large Language Models (LLMs) (FLAN T5, Llama and Phi) to automate the identification of these symptom categories. Results: Our findings show that all LLMs are 60% more efficient than word match with Flan leading in precision (average F1: 0.65, precision: 0.78), excelling in the extraction of more rare symptoms like "sleep problem

关键词： Pediatrics

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Position paper: Extending Credibility Assessment of In Silico Medicine Predictors to Machine Learning Predictors

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IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Viceconti, Marco Lanubile, Filippo Carbonaro, Antonella Mellone, Sabato Curreli, Cristina Aldieri, Alessandra Ranciati, Saverio Montanari, Angela Department of Industrial Engineering Italy Irccs Istituto Ortopedico Rizzoli Medical Technology Lab IT Bologna Italy Department of Informatics Italy Department of Computer Science and Engineering Italy Medical Technology Lab Irccs Istituto Ortopedico Rizzoli IT Bologna Italy PolitoBIOMedLab Department of Mechanical and Aerospace Engineering Italy Department of Statistical Sciences Italy

There are several situations where it would be convenient if a quantity of interest essential to support a medical or regulatory decision could be predicted as a function of other measurable quantities rather than measured experimentally. To do so, we need to ensure that in all practical cases, the predicted value does not differ from what we would measure experimentally by more than an acceptable threshold, defined by the context in which that quantity of interest is used in the decision-making process. This is called Credibility Assessment. Initial work, which guided the elaboration of the first technical standard on the topic (ASME VV-40:2018), focused on predictive models built from available mechanistic knowledge of the phenomenon of interest. For this class of predictive models, sometimes called biophysical models, a credibility assessment practice based on the so-called verification, Validation, Uncertainty, Quantification and Applicability (VVUQA) analysis is accepted. Through theoretical considerations, this position paper aims to summarise a complex debate on whether such an approach can be extended to predictive models built without any mechanistic knowledge (machine learning (ML) predictors). We conclude that the VVUQA can be extended to ML-based predictors;however, since there is no certainty that the features used to predict the quantity of interest are necessary and sufficient, according to the VVUQA framework, such credibility assessment is limited to the test sets used for the validation studies. This calls for a Total Product Life Cycle approach, where periodic retesting of ML-based predictors is part of post-marketing surveillance to ensure that no "unknown bias"may play a role. © 2013 IEEE.

关键词： Adversarial machine learning

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Finding Optimal Sensor Placements for Traffic Data Reconstruction Using QR Pivoting and Deep Reinforcement Learning

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Transportation Research Procedia 2025年 82卷 2885-2897页

作者： Hoang Dat Pham Sharath MN Ahmed Ashraf Babak Mehran Manitoba Learning and AI Research Department of Electrical and Computer Engineering University of Manitoba Winnipeg Canada R3T5V6 Urban Mobility and Transportation Informatics Group Department of Civil Engineering University of Manitoba Winnipeg Canada R3T5V6

This paper studies and presents data-driven methods for finding rankings of traffic links in a network for optimal traffic data reconstruction based on measurements taken from a subset of links. The link ranking represents the importance of respective links in terms of reconstructing traffic information from sparsely placed sensors, connected vehicles, or other state-of-the-art methods. We first present a baseline method based matrix factorization of the eigen-vector basis matrix, followed by column pivoting. Moreover, we propose a reinforcement learning framework to improve the ranking method when the traffic data is used for the purpose of routing. This study utilizes dynamic traffic data that is observed and estimated from simulation.

关键词： Traffic Data Data Reconstruction Sensor Placement Matrix Factorization Reinforcement Learning

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Tau Pet Harmonization via Surface-Based Diffusion Model

Tau Pet Harmonization via Surface-Based Diffusion Model

引用

IEEE International Symposium on Biomedical Imaging

作者： Jiaxin Yue Jianwei Zhang Lujia Zhong Yonggang Shi For the Alzheimer's Disease Neuroimaging Initiative, and the Health and Aging Brain Study (HABS-HD) Study Team Stevens Neuroimaging and Informatics Institute University of Southern California Los Angeles CA USA Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The heterogeneity inherent in tau positron emission tomog-raphy (PET) imaging data across different tracers challenges the integration of multi-site tau PET data, thereby necessitating the trustful harmonization technique for a better utilization of the emerging large-scale datasets. Unlike other imaging modalities, the harmonization among multi-site tau PET data involves more than intensity mapping but contains in-tricate pattern alterations attributed to tracer binding proper-ties, which makes the existing statistical methods inadequate. Meanwhile, the effective data preprocessing is required to eliminate the artifacts caused by off-target binding and partial volume effect for meaningful comparison and harmonization. In this paper, we propose a systematic tau PET harmonization framework that involves the surface-based data preprocessing and diffusion model for generating the vertex-wise mapping between multi-site tau standardized uptake value ra-tio (SUVR) on the cortical surface. In the experiments, using large-scale Alzheimer's Disease Neuroimaging Initiative (ADNI) and Health and Aging Brain Study-Health Dispari-ties (HABS-HD) data with different tracers, we demonstrate our method can successfully achieve harmonization by gen-erating the SUVR maps with consistent pattern distributions and persevering the individual variability.

关键词： Neuroimaging Visualization Systematics Statistical analysis Data preprocessing Positrons Diffusion models Positron emission tomography Alzheimer's disease Biomedical imaging

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LLGS: Unsupervised Gaussian Splatting for Image Enhancement and Reconstruction in Pure Dark Environment

arXiv

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arXiv 2025年

作者： Wang, Haoran Huang, Jingwei Yang, Lu Deng, Tianchen Zhang, Gaojing Li, Mingrui The School of Engineering and Informatics University of Sussex United Kingdom The Department of Automation Engineering University of Electronic Science and Technology of China China The Department of Automation Shanghai Jiao Tong University China The Department of Computer Science Dalian University of Technology China

3D Gaussian Splatting has shown remarkable capabilities in novel view rendering tasks and exhibits significant potential for multi-view optimization. However, the original 3D Gaussian Splatting lacks color representation for inputs in low-light environments. Simply using enhanced images as inputs would lead to issues with multi-view consistency, and current single-view enhancement systems rely on pre-trained data, lacking scene generalization. These problems limit the application of 3D Gaussian Splatting in low-light conditions in the field of robotics, including high-fidelity modeling and feature matching. To address these challenges, we propose an unsupervised multi-view stereoscopic system based on Gaussian Splatting, called Low-Light Gaussian Splatting (LLGS). This system aims to enhance images in low-light environments while reconstructing the scene. Our method introduces a decomposable Gaussian representation called M-Color, which separately characterizes color information for targeted enhancement. Furthermore, we propose an unsupervised optimization method with zero-knowledge priors, using direction-based enhancement to ensure multi-view consistency. Experiments conducted on real-world datasets demonstrate that our system outperforms state-of-the-art methods in both low-light enhancement and 3D Gaussian Splatting. © 2025, CC BY-NC-ND.

关键词： Stereo image processing

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Reliable Service Recommendation: A Multi-modal Adversarial Method for Personalized Recommendation under Uncertain Missing Modalities

引用

IEEE Transactions on Services Computing 2025年

作者： Chen, Junyang Yang, Ruohan Guo, Jingcai Wang, Huan Wu, Kaishun Zhang, Liangjie Shenzhen University College of Computer Science and Software Engineering China Huazhong Agricultural University College of Informatics Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong DSA Thrust and IoT Thrust Center China

Personalized recommendation is of paramount importance in online content platforms like Kuai and Tencent. To ensure accurate recommendations, it is crucial to consider multi-modal information in both items and user-user/item interactions. While existing works on multimedia recommendation have made strides in leveraging multi-modal contents to enrich item representations, many of them overlook the practical scenario of multiple modality missing. As a result, the performance of recommendation systems can be significantly compromised in such cases. In this paper, we introduce a novel multi-modal adversarial method called MMAM, which aims to provide reliable personalized recommendation services even in the presence of uncertain missing modalities. The core idea behind MMAM is to design a generator that can effectively encode both user-user/item interactions and multi-modal contents, taking into account various missing cases. The generator is trained to learn transferable features from different combinations of missing modalities in order to deceive a discriminative classifier. Additionally, we propose a modal discriminator that can classify the missing cases of multi-modalities, further enhancing the capability of the model. Moreover, a well-equipped predictor utilizes the transferable features to predict potential user interests. To improve the prediction accuracy, we design a type discriminator that enhances the classification of link types. By employing a mini-max game between the generator and the discriminators, MMAM successfully obtains transferable features that encompass multi-modal contents, even when facing uncertain missing modalities. We conduct extensive experiments on industrial datasets, including Kuai and Tencent. Comparing with state-of-the-art approaches, MMAM achieves improvements in personalized recommendation tasks under uncertain missing modalities. MMAM holds promise for enhancing multi-modal personalized recommendations in real-world applications

关键词： Discriminators

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