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31st International conference on Multimedia Modeling

作者： Zhao, Hui Qi, Na Zhu, Qing Lin, Xiumin Beijing Univ Technol Coll Comp Sci Sch Software Engn Natl Pilot Software Coll Beijing 100124 Peoples R China

ISBN: (纸本)9789819620708;9789819620715

Reconstructing Hyperspectral images (HSIs) from Coded Aperture Snapshot Spectral Imaging (CASSI) is an important yet challenging task. The core issue lies in recovering reliable and detailed 3D HSI cube from 2D measurement. Deep unfolding framework which alternates between solving data subproblems and prior subproblems has made satisfactory progress in HSIs reconstruction task. However, current methods do not fully utilize the spatial spectral prior of HSIs. To solve this problem and further enhance the spectral-spatial representation capabilities in the prior subproblems, we propose a Spatial-Spectral Correlation Transformer Based on Deep Unfolding Framework (SSCDUF). Specifically, we introduce a multi-scale Spatial-Spectral Correlation Fusion Transformer (SSCT) module that simultaneously utilize the similarity and correlation of spectral features as well as local and non-local spatial features, jointly using spatial and spectral prior to enhance feature representation. Moreover, we further propose an Adaptive Aggregation Skip Connection (AASC) module to adaptively aggregate spatial and spectral features in multiple scales. Extensive experimental results on both simulated and real scenes demonstrate that SSCDUF outperforms the state-of-the-art methods in terms of quantitative metrics while maintaining low parameter costs and runtime.

关键词： Hyperspectral image reconstruction Deep Unfolding Framework Spatial-Spectral Transformer Adaptive Aggregation Skip Connection

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Research on depth estimation and human body reconstruction effects in single-image scene reconstruction 2

Research on depth estimation and human body reconstruction e...

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2nd International conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Zhou, Xiuyuan Li, Yuanzhen Zhu, Yaling Liu, Ruilin Yang, Lina Jing, Yongxia Lanzhou Institute of Technology Gansu Lanzhou730030 China Yunnan University Yunnan Kunming650091 China Qiongtai Normal University Hainan Haikou571100 China

ISBN: (纸本)9781510689053

With the rapid development of digital technology and deep learning, recovering 3D scene information and reconstructing human bodies from a single image has become a focal point of research in computer vision and computer graphics. This technology has also found widespread application in fields such as cultural relic restoration, autonomous driving, virtual reality, and medical image analysis. In this paper, we explore the challenges posed by the network's contextual perception abilities and the influence of loss functions, which can lead to issues like incomplete depth structures, depth drift, and texture copying. To overcome these obstacles, we propose refined methods that produce highly accurate and structurally sound depth estimates, effectively resolving problems such as texture copying and depth drift. Our methods demonstrate strong generalization capabilities in human depth estimation models, enabling precise depth estimation across various scenarios. © 2025 SPIE.

关键词： Virtual reality

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9th International Skin Imaging Collaboration Workshop, ISIC 2024, 7th International Workshop on Interpretability of Machine Intelligence in Medical image Computing, iMIMIC 2024, Embodied AI and Robotics for HealTHcare Workshop, EARTH 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

9th International Skin Imaging Collaboration Workshop, ISIC ...

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9th International Skin Imaging Collaboration Workshop, ISIC 2024, 7th International Workshop on Interpretability of Machine Intelligence in Medical image Computing, iMIMIC 2024, Embodied AI and Robotics for HealTHcare Workshop, EARTH 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031776090

The proceedings contain 23 papers. The special focus in this conference is on Skin Imaging Collaboration, Interpretability of Machine Intelligence in Medical image Computing, Embodied AI and Robotics for HealTHcare Workshop and MICCAI Workshop on Distributed, Collaborative and Federated Learning. The topics include: DeCaF 2024 Preface;i2M2Net: Inter/Intra-modal Feature Masking Self-distillation for incomplete Multimodal Skin Lesion Diagnosis;from Majority to Minority: A Diffusion-Based Augmentation for Underrepresented Groups in Skin Lesion Analysis;segmentation Style Discovery: Application to Skin Lesion images;a Vision Transformer with Adaptive Cross-image and Cross-Resolution Attention;lesion Elevation Prediction from Skin images Improves Diagnosis;DWARF: Disease-Weighted Network for Attention Map Refinement;PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans;Detecting Unforeseen data Properties with Diffusion Autoencoder Embeddings Using Spine MRI data;interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis;TextCAVs: Debugging Vision Models Using Text;evaluating Visual Explanations of Attention Maps for Transformer-Based Medical Imaging;Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI;EndoGS: Deformable Endoscopic Tissues reconstruction with Gaussian Splatting;VISAGE: Video Synthesis Using Action Graphs for Surgery;a Review of 3D reconstruction Techniques for Deformable Tissues in Robotic Surgery;SurgTrack: CAD-Free 3D Tracking of Real-World Surgical Instruments;MUTUAL: Towards Holistic Sensing and Inference in the Operating Room;Complex-Valued Federated Learning with Differential Privacy and MRI Applications;enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications;federated Impression for Learning with Distributed Heterogeneous data;A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation;probing the Effic

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Compressive Imaging reconstruction via Conditional Diffusion Model With Augmented Measurements

Compressive Imaging Reconstruction via Conditional Diffusion...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Emmanuel Martinez Leon Suarez Romario Gualdrón-Hurtado Roman Jacome Henry Arguello Department of Systems and Informatics Engineering Universidad Industrial de Santander Bucaramanga Colombia Department of Electrical Electronics and Telecommunications Engineering Universidad Industrial de Santander Bucaramanga Colombia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Compressive imaging (CI) consists of reconstructing images from incomplete observed data. The reconstruction process involves solving an ill-posed inverse problem which is highly dependent on the number of real measurements, with a greater number of measurements typically leading to more accurate reconstructions. Due to their ability to learn data distributions, diffusion models (DM) have emerged as promising techniques for various inverse problems. Mainly, DMs solve inverse problems by conditioning the generation process to the acquired measurements. In this work, we introduce a new approach to improve this conditioning by exploiting synthetic measurements, which come from a synthetic sensing matrix. Synthetic measurements are estimated from real data via a neural network. The combined real and synthetic measurements form an augmented set, which is input into the conditional DM to enhance reconstruction capacity. Computational experiments demonstrate that augmenting measurements with the conditional DM improves performance compared to using only real measurements.

关键词： image coding Inverse problems Imaging Signal processing Diffusion models Robustness Sensors Noise measurement Speech processing image reconstruction

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Generative adversarial networks in medical image reconstruction: A systematic literature review

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Computers in Biology and Medicine 2025年 191卷 110094-110094页

作者： Hussain, Jabbar Båth, Magnus ivarsson, Jonas Dept. of Applied IT University of Gothenburg Forskningsgången 6 417 56 Sweden Department of Medical Radiation Sciences University of Gothenburg Sweden

Purpose: Recent advancements in generative adversarial networks (GANs) have demonstrated substantial potential in medical image processing. Despite this progress, reconstructing images from incomplete data remains a challenge, impacting image quality. This systematic literature review explores the use of GANs in enhancing and reconstructing medical imaging data. Method: A document survey of computing literature was conducted using the ACM Digital Library to identify relevant articles from journals and conference proceedings using keyword combinations, such as "generative adversarial networks or generative adversarial network," "medical image or medical imaging," and "image reconstruction." Results: Across the reviewed articles, there were 122 datasets used in 175 instances, 89 top metrics employed 335 times, 10 different tasks with a total count of 173, 31 distinct organs featured in 119 instances, and 18 modalities utilized in 121 instances, collectively depicting significant utilization of GANs in medical imaging. The adaptability and efficacy of GANs were showcased across diverse medical tasks, organs, and modalities, utilizing top public as well as private/synthetic datasets for disease diagnosis, including the identification of conditions like cancer in different anatomical regions. The study emphasized GAN's increasing integration and adaptability in diverse radiology modalities, showcasing their transformative impact on diagnostic techniques, including cross-modality tasks. The intricate interplay between network size, batch size, and loss function refinement significantly impacts GAN's performance, although challenges in training persist. Conclusions: The study underscores GANs as dynamic tools shaping medical imaging, contributing significantly to image quality, training methodologies, and overall medical advancements, positioning them as substantial components driving medical advancements. © 2025 The Authors

关键词： Medical image processing

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An Attention-Based Generative Adversarial Network for Limited-Angle CT reconstruction

An Attention-Based Generative Adversarial Network for Limite...

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International conference on Computer and Automation Engineering, ICCAE

作者： Haytham A. Ali Hiroyuki Kudo Systems and Information Engineering University of Tsukuba Tsukuba Japan Faculty of Science Sohag University Egypt

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Reconstructing high-quality computed tomography (CT) images from limited-angle projections is a challenging and ill-posed problem, often resulting in severe artifacts and loss of structural details. Traditional analytical methods, such as Filtered Back Projection (FBP), struggle with incomplete data, while existing deep learning approaches face limitations in generalization and reliance on extensive paired datasets. To address these challenges, we propose a novel Generative Adversarial Network (GAN)-based framework comprising a U-Net-inspired generator enhanced with residual blocks and selfattention mechanisms, coupled with a PatchGAN discriminator. The generator effectively captures long-range dependencies and structural features critical for artifact removal and reconstruction accuracy, while the PatchGAN discriminator enforces local texture realism. Additionally, a perceptual loss derived from a pretrained VGG network preserves fine anatomical details and high-level semantic consistency. Extensive evaluations on clinical datasets demonstrate the superiority of our method over state-of-the-art techniques. Quantitative metrics, including PSNR, SSIM, and MSE, confirm significant improvements, and qualitative results showcase the effective suppression of artifacts and recovery of fine structural details.

关键词： Measurement Deep learning Visualization Accuracy Computed tomography Semantics Generative adversarial networks Generators image reconstruction Faces

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Proceedings of SPIE -image reconstruction from incomplete data iv

Proceedings of SPIE -Image Reconstruction from Incomplete Da...

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image reconstruction from incomplete data iv

ISBN: (纸本)0819463957

The proceedings contain 20 papers. The topics discussed include: turbulence profiling using extended objects for slope detection and ranging (SLODAR);mitigating atmospheric effects in high-resolution infrared surveillance imagery with bispectral speckle imaging;restoration of nonuniformly wrapped images using accurate frame by frame shiftmap accumulation;three-dimensional image reconstruction in variable density acoustic tomography;imaging with singular electromagnetic beam;comparative study of projection/back-projection schemes in cryo-EM tomography;intensity diffraction tomography with a novel scanning protocol;quantifying and correcting motion artifacts in MRI;the optimal reconstruction from blurred and nonuniformly sampled data based on the optimum discrete approximation minimizing various worst-case measures of error;and analysis of gravel river beds using three-dimensional laser scanning.

关键词： image reconstruction

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PGRID: Power Grid reconstruction in Informal Developments Using High-Resolution Aerial imagery

PGRID: Power Grid Reconstruction in Informal Developments Us...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Simone Fobi Nsutezo Amrita Gupta Duncan Kebut Seema Iyer Luana Marotti Rahul Dodhia Juan M. Lavista Ferres Anthony Ortiz Microsoft AI for Good Research Lab HOTOSM USA for UNHCR

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

As of 2023, a record 117 million people have been dis-placed worldwide, more than double the number from a decade ago [22]. Of these, 32 million are refugees under the UNHCR's mandate, with 8.7 million residing in refugee camps. A critical issue faced by these populations is the lack of access to electricity, with 80% of the 8.7 million refugees and displaced persons in camps globally relying on traditional biomass for cooking and lacking reliable power for essential tasks such as cooking and charging phones. Often, the burden of collecting firewood falls on women and children, who frequently travel up to 20 kilometers into dan-gerous areas, increasing their vulnerability. [7] Electricity access could significantly alleviate these challenges, but a major obstacle is the lack of accurate power grid infrastructure maps, particularly in resource-constrained environments like refugee camps, needed for energy access planning. Existing power grid maps are often outdated, incomplete, or dependent on costly, complex technologies, limiting their practicality. To address this issue, PGRID is a novel application-based approach, which utilizes high-resolution aerial imagery to detect electrical poles and segment electrical lines, creating precise power grid maps. PGRID was tested in the Turkana region of Kenya, specifically the Kakuma and Kalobeyei Camps, cov-ering 84 km 2 and housing over 200,000 residents. Our findings show that PGRID delivers high-fidelity power grid maps especially in unplanned settlements, with F1-scores of 0.71 and 0.82 for pole detection and line segmentation, respectively. This study highlights a practical application for leveraging open data and limited labels to improve power grid mapping in unplanned settlements, where the growing number of displaced persons urgently need sustainable energy infrastructure solutions.

关键词： image segmentation Accuracy Electricity Source coding Power grids Planning Resource management Reliability Open data image reconstruction

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A Novel Self-Supervised Contrastive Learning Framework for Masked EEG Motor imagery Modeling

A Novel Self-Supervised Contrastive Learning Framework for M...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kunkun Zhang Qianwei Zhou Haigen Hu College of Computer Science and Technology Zhejiang University of Technology Hangzhou PR China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Electroencephalography (EEG) is vital for brain-computer interfaces (BCIs) due to its non-invasive approach and high temporal resolution data capabilities, amid challenges such as data scarcity and the need for extensive labeling. Significant inter-individual variability in EEG signals further limits model generalization. Concurrently, the use of self-supervised pre-training, particularly through masked modeling, is gaining traction in time series analysis to mitigate labeling costs. Although this method involves reconstructing masked signal from unmasked series, random masking can disrupt critical temporal variations, complicating effective representation learning. We thus introduce SSL-MEMI, a novel self-supervised contrastive learning framework for masked EEG motor imagery modeling, integrating Domain Adaptive Alignment (DAA) and Multi-View Temporal-spatial Attention module (MTSA) to effectively handle EEG variability. This framework utilizes manifold-based masking to reconstruct original sequences from masked series, thereby enhancing classification accuracy. When tested on the BCI Competition iv and High Gamma datasets, SSL-MEMI outperforms existing methods, achieving top accuracies and demonstrating superior domain adaptation through reduced Global ${\mathcal{A}}$-distance scores. This study advances EEG classification and indicates broader applications for self-supervised learning in biomedical signal processing. The source code is available at https://***/KunKun-Zhang/***.

关键词： Manifolds Adaptation models Accuracy Time series analysis Contrastive learning Brain modeling Motors Electroencephalography Labeling image reconstruction

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Improved image reconstruction from multiple frames

Improved image reconstruction from multiple frames

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conference on image reconstruction from incomplete data iv

作者： Testorf, Markus E. Fiddy, Michael Dartmouth Coll Thayer Sch Engn 8000 Cummings Hall Hanover NH 03755 USA Univ N Carolina Ctr Optoelect & Opt Commun Charlotte NC USA

ISBN: (纸本)0819463957

A method is described for reconstructing images from incomplete data sets if subsets of the data are obtained with different data acquisition systems. The proposed method is able to incorporate prior information about the object into the reconstruction process. The general scheme is applied to construct improved images from multiple low resolution frames of the same scene, where each frame is obtained with an imaging system characterized by a different response function. The working principle is demonstrated with synthetic data for a set of generic imaging systems. Additional attention is given to the potential of the inherent superresolution capabilities of this method in the context of compressed image representations.

关键词： computational imaging resolution enhancement image reconstruction multiple image frames

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