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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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GTA-HDR: A Large-Scale Synthetic dataset for HDR image reconstruction

GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Recon...

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2025 IEEE/CvF Winter conference on Applications of Computer vision, WACv 2025

作者： Barua, Hrishav Bakul Stefanov, Kalin Wong, KokSheik Dhall, Abhinav Krishnasamy, Ganesh Monash University Australia Tcs Research India

ISBN: (纸本)9798331510831

High Dynamic Range (HDR) content (i.e., images and videos) has a broad range of applications. However, capturing HDR content from real-world scenes is expensive and time-consuming. Therefore, the challenging task of reconstructing visually accurate HDR images from their Low Dynamic Range (LDR) counterparts is gaining attention in the vision research community. A major challenge is the lack of datasets, which capture diverse scene conditions (e.g., lighting, weather, locations) and various image features (e.g., color, contrast, saturation). To address this gap, we introduce GTA-HDR, a large-scale synthetic dataset of photo-realistic HDR images sampled from the GTA-v video game. We perform thorough evaluation of the proposed dataset, which enables significant qualitative and quantitative improvements of the state-of-the-art HDR image reconstruction methods. Furthermore, we demonstrate the effectiveness of the proposed dataset and its impact on additional computer vision tasks including 3D human pose estimation, human body part segmentation, and holistic scene segmentation. The dataset, data collection pipeline, and evaluation code are available at: https://***/HrishavBakulBarua/GTA-HDR. © 2025 IEEE.

关键词： Deep learning

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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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GTA-HDR: A Large-Scale Synthetic dataset for HDR image reconstruction

GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Recon...

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IEEE Workshop on Applications of Computer vision (WACv)

作者： Hrishav Bakul Barua Kalin Stefanov KokSheik Wong Abhinav Dhall Ganesh Krishnasamy Monash University & TCS Research Monash University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

关键词： image segmentation Computer vision video games Three-dimensional displays Accuracy Pose estimation Pipelines data collection image reconstruction Synthetic data

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18th International conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

18th International Conference on Wireless Artificial Intelli...

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18th International conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

ISBN: (纸本)9783031714696

The proceedings contain 108 papers. The special focus in this conference is on Wireless Artificial Intelligent Computing Systems and Applications. The topics include: Profinder: Towards Professionals Recognition on Mobile Devices for Users with Cognitive Decline;truthful Double Auction-Based Resource Allocation Mechanisms for Latency-Sensitive Applications in Edge Clouds;v2ICooper: Toward vehicle-to-Infrastructure Cooperative Perception with Spatiotemporal Asynchronous Fusion;recommendation-Aware Collaborative Edge Caching Strategy in the Internet of vehicles;towards Robust Internet of vehicles Security: An Edge Node-Based Machine Learning Framework for Attack Classification;device-Edge-Cloud Collaborative video Stream Processing and Scheduling Strategy Based on Deep Reinforcement Learning;consistent Low-Latency Scheduling for Microsecond-Scale Tasks in data Centers;multi-scale data reconstruction Based Policy Optimization Algorithm for Skill Learning;Joint Optimization Design of Intelligence Reflecting Surface Assisted MU-MISO System Based on Deep Reinforcement Learning;Secure Motion verification for High Altitude Platforms with a Hybrid AOA-TDOA-FDOA Scheme;variational Autoencoder Based Automatic Clustering for Multivariate Time Series Anomaly Detection;deep Reinforcement Learning Based Economic Dispatch with Cost Constraint in Cyber Physical Energy System;inceptionNeXt Network with Relative Position Information for Microexpression Recognition;adMarks: image Steganography Based on Adversarial Perturbation;a Review on Binary Code Analysis datasets;low-Cost Robot Path Planning Mechanism for Escaping from Dead Ends;towards Communication-Efficient Collaborative Perception: Harnessing Channel-Spatial Attention and Knowledge Distillation;improving Anomaly Scene Recognition with Large vision-Language Models;ReSU-Net: State Space Model for 3D Abdominal Multi-organ Segmentation;autocue : Targeted Textual Adversarial Attacks with Adversarial Prompts;universal Sign Language

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18th International conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

18th International Conference on Wireless Artificial Intelli...

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18th International conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

ISBN: (纸本)9783031714665

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18th International conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

18th International Conference on Wireless Artificial Intelli...

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18th International conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

ISBN: (纸本)9783031714634

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