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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data"

共 14740 条记录，以下是211-220 订阅

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DiffuCE: Expert-Level CBCT image Enhancement Using a Novel Conditional Denoising Diffusion Model with Latent Alignment

DiffuCE: Expert-Level CBCT Image Enhancement Using a Novel C...

引用

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Fang-Yi Su Tzu-Hung Chang Jung-Hsien Chiang National Cheng Kung University Taiwan

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Cone-Beam Computed Tomography (CBCT) has gar-nered significant attention due to lower radiation dosage and faster scanning time, which has been widely used in clinical applications for decades. However, its poor image quality is always challenging to clinical experts. To address this problem, we propose our work DiffuCE, $a$ Diffusion model framework for CBCT Enhancement. The main contributions of our work are three-fold: (1) Increased Gen-eralizability: Our training data exclusively comprises pixel space data, eliminating the necessity for additional imaging machine settings. This emphasizes the model's ability to generalize effectively across diverse conditions. (2) Effi-cient Training: Rather than starting from scratch, our approachfine-tunesfrom a well-established foundation model. This illustrates the viability of efficient training strategies for medical image restoration tasks, optimizing resource utilization. (3) Competitive Performance: DiffuCE exhibits outstanding performance, excelling in FID and LPIPS with 0.01 and 36.99 ahead of the second place in the private set. In the public dataset, DiffuCE has a competitive performance compared to other SOTAs. Moreover, in expert assessments, DiffuCE achieves the highest score of 7.06 for overall satisfaction, which is 1.38 ahead of the second place, affirming its performance from a clinical stand-point. Codes are available at https://***/lzh107u/DiffuCE

关键词： Training Statistical analysis Computed tomography Noise reduction Training data Diffusion models image restoration Resource management Radiation dosage image reconstruction

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Rendering 3D CT Scans through 3D Gaussian Splatting Initialized with Points Sampled by Cube-Based Neural Radiance Fields

Rendering 3D CT Scans through 3D Gaussian Splatting Initiali...

引用

International conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Sanghyuk Roy Choi Chanhoe Gu Sun Jae Baek Minhyeok Lee Department of Intelligent Semiconductor Engineering Chung-Ang University Seoul Korea School of Electrical and Electornics Engineering Chung-Ang University Seoul Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Inthis paper, we present Cube-Cloud 3D Gaussian Splatting (Cube-Cloud 3DGS), a novel framework designed to render medical image that inherently structured with three fixed axes. The methods such as COLMAP, Neural Radiance Field (NeRF), and 3D Gaussian Splatting are unsuitable for medical image reconstruction due to the lack of diverse viewpoints. To address this challenge, we propose Cube-Cloud 3DGS that leverages Cube-based Neural Radiance Field (CuNeRF) for cube-based sampling to generate point clouds from medical data. CuNeRF in Cube-Cloud 3DGS generates point cloud and renders images through various viewpoints which can be used as camera poses. We integrate the point cloud with 3D Gaussian Splatting that is initializing 3D gaussians. By utilizing the viewpoints extracted from CuNeRF, the parameters of 3D gaussians are refined. Cube-Cloud 3DGS renders images through its 3D gaussians while traditional models fail to render based on medical images. We evaluated Cube-Cloud 3DGS on the Kidney and Kidney Tumor Segmentation (KiTS23) dataset, demonstrating that our model reconstructs 3D medical volumes effectively. Therefore, our model resolves the limitation focusing on the internal features for medical images. Our model achieves higher performance of 2.707 in PSNR and 0.0504 in SSIM over existing 3D Gaussian Splatting.

关键词： Point cloud compression Solid modeling Three-dimensional displays Focusing Neural radiance field Rendering (computer graphics) image reconstruction Kidney Biomedical imaging Tumors

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Some Intriguing Observations on the Learnt Matrices in Deep Unfolded Networks

Some Intriguing Observations on the Learnt Matrices in Deep ...

引用

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

作者： Kartheek Kumar Reddy Nareddy Inbasekaran Perumal Chandra Sekhar Seelamantula Department of Electrical Engineering Indian Institute of Science Bengaluru Institute of Data Science German Aerospace Center Jena Germany Department of Electronics and Communication Engineering National Institute of Technology Mangaluru Karnataka

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep-unfolded networks (DUNs) have set new performance benchmarks in fields such as compressed sensing, image restoration, and wireless communications. DUNs are built from conventional iterative algorithms, where an iteration is transformed into a layer/block of a network with learnable parameters. Despite their huge success, the reasons behind their superior performance over their iterative counterparts are not fully understood. This paper focuses on enhancing the explainability of DUNs by investigating potential reasons behind their superior performance over traditional iterative methods. We concentrate on the Learnt Iterative Shrinkage-Thresholding Algorithm (LISTA), a foundational contribution that achieves sparse recovery with significantly fewer layers than its iterative counterpart, ISTA. Our findings reveal that the learnt matrices in LISTA always have Gaussian distributed entries regardless of whether the sensing matrix is random Gaussian, Bernoulli, exponential, or uniform. The findings also show that the singular values of the learnt matrices exceed unity, despite which, the reconstruction scheme is stable. We conjecture that the activation function may have a role to play in ensuring stability. We also present an unbiasing technique that substantially improves the sparse recovery performance by reestimating the amplitudes based on the converged support.

关键词： Training Wireless communication Accuracy Superresolution Signal processing algorithms Gaussian distribution Iterative algorithms Sparse matrices image reconstruction Convergence

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PrivateEye: In-Sensor Privacy Preservation Through Optical Feature Separation

PrivateEye: In-Sensor Privacy Preservation Through Optical F...

引用

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Adith Boloor Weikai Lin Tianrui Ma Yu Feng Yuhao Zhu Xuan Zhang Washington University in St. Louis St. Louis MO USA University of Rochester Rochester NY USA Shanghai Jiao Tong University Shanghai China Northeastern University Boston MA USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

We address privacy issues in applications where images captured by an edge device (camera) are sent to the cloud for inference on utility tasks such as classification. Sending raw images to the cloud exposes them to data sniffing attacks and misuse by untrusted third-party service providers beyond the user's intended tasks. We propose an encoding scheme that not only evades direct visual inspection to the images or image reconstruction, but also prevents sensitive information from being ascertained. Unlike commonly used adversarial learning approaches, the proposed method is two-fold: first, it uses a diffractive optical neural network to spatially separate features corresponding to different tasks on the sensor plane in the optical domain. Then only the pixels corresponding to the utility task region are read. This encoding ensures that private features are never digitally stored on the edge device, thereby preventing privacy leakage. The proposed method successfully reduces the privacy retrieval in binary tasks with minimal accuracy loss (~ 2%) of the utility task, while reducing private task accuracy by ~ 35% and defending against reconstruction attacks with SSIM score of 0.43.

关键词： Privacy Cloud computing Optical diffraction Accuracy Optical computing Optical fiber networks Optical imaging Optical sensors Nonlinear optics image reconstruction

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Tumor Segmentation in Brain MR images Using Res-Unet with Multimodal Fusion

Tumor Segmentation in Brain MR Images Using Res-Unet with Mu...

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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe conference on

作者： Siyuan Yin Yuanqian Wang Shuxin Wang Jie Chang Jinhao Zhu Wei Lu School of Medical Information Wannan Medical College Wuhu China College of Traditional Chinese Medicine Tianjin University of Traditional Chinese Medicine Tianjin China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

Early diagnosis of brain tumors presents significant challenges. Single-modality imaging often suffers from limitations, such as incomplete structural representation and a lack of complementary information, which reduce segmentation accuracy and complicate the identification of complex tumor scenarios. Additionally, as network depth increases, performance degradation becomes a notable issue. This research presents a multimodal fusion approach that efficiently combines complementary information from various MRI modalities to overcome these challenges. This approach mitigates the limitations of single-modality data, providing the model with richer and more comprehensive features, thereby enhancing the characterization of tumor regions. The Res-UNet model is employed to merge the benefits of the U-Net architecture for biomedical image segmentation with the strengths of residual modules. This combination effectively tackles the degradation issue linked to increased network depth, markedly enhancing segmentation accuracy, especially in defining tumor boundaries and distinguishing different tumor regions. The experimental outcomes on public datasets reveal that the proposed model surpasses existing ones in all evaluation metrics. It can preserve lesion details and precisely segment tumor boundaries. This advancement offers substantial support for clinical brain tumor diagnosis and contributes to the fields of medical imaging, bioinformatics, and related disciplines.

关键词： Degradation image segmentation Adaptation models Biological system modeling Magnetic resonance imaging Brain tumors Brain modeling data models Clinical diagnosis Tumors

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Using Aerial Videos for Natural Disaster Damage Assessment

Using Aerial Videos for Natural Disaster Damage Assessment

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International Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Moez Krichen Mohamed S. Abdalzaher Mostafa Shaaban Raafat Aburukba Al-Baha University Al-Baha Saudi Arabia ReD CAD Laboratory University of Sfax Sfax Tunisia Electrical Engineering Department American University of Sharjah Sharjah UAE Seismology Department National Research Institute of Astronomy and Geophysics Cairo Egypt Energy Water and Sustainable Environment Research Center American University of Sharjah Computer Science and Engineering Department American University of Sharjah Sharjah UAE

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Rapid damage assessment is crucial in the aftermath of natural disasters, enabling emergency responders to efficiently allocate resources and facilitate recovery efforts. This paper investigates the potential of utilizing satellite imagery and drone (UAV) technology to enhance the speed and accuracy of disaster damage assessment. We explore the various applications of satellite imagery, from pre-disaster risk assessment to post-disaster situational awareness and reconstruction monitoring. The advantages of UAV-captured data for detailed, on-the-ground damage evaluation are also discussed. The paper then delves into the role of advanced analytics, such as convolutional neural networks (CNNs) and semantic segmentation, in automating the analysis of satellite and UAV imagery for rapid damage mapping and classification. Additionally, we highlight the value of integrating crowdsourced data with remote sensing inputs to further improve situational awareness and damage reporting. The paper also examines the use of 3- D reconstruction techniques that leverage multiple data sources to create detailed models for damage assessment and recovery planning. By synthesizing the latest advancements in satellite technology, drone capabilities, and artificial intelligence, this paper presents a comprehensive framework for leveraging these tools to enable more effective and efficient disaster response and recovery operations. The findings and recommendations outlined in this work can inform both academic research and practical applications in the field of disaster management.

关键词： Disasters Soft sensors Semantic segmentation Disaster management Autonomous aerial vehicles Satellite images Resource management Remote sensing image reconstruction Resilience

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image Compression Algorithm Based on Region of Interest Extraction for Unmanned Aerial Vehicles Communication

Image Compression Algorithm Based on Region of Interest Extr...

引用

IEEE Information Technology and Mechatronics Engineering conference (ITOEC)

作者： Yanxia Liang Tong Jia Xin Liu Huanhuan Zhang Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and Telecommunications Xi’an China College of Information Engineering Xi’an Eurasia University Xi’an China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

The flexibility and maneuverability of unmanned aerial vehicles (UAVs) have led to their widespread use, but the challenge of limited storage space and bandwidth in emergency scenarios necessitates efficient image compression techniques. In this research, we propose an image compression algorithm tailored for UAV communication, termed ROIE-UC (Region of Interest Extraction for UAV Communication). Firstly, image pixels are clustered into super pixel blocks using the Simple Linear Iterative Clustering (SLIC). Secondly, these super pixels are grouped into regions of interest (ROI) using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The image is then segmented into ROI and non-ROI areas based on these clusters. Lossless compression is applied to the ROI, while lossy compression with a high ratio is used for non-ROI regions. At the receiving end, the image is decompressed and reconstructed. Experimental results show that the ROIE-UC achieves a Peak Signal-to-Noise Ratio (PSNR) of 46.37 dB and a Structural Similarity Index (SSIM) of 99.94% for the ROI part, with compression ratios of 27, 25, and 14 for the whole image. Compared to the JPEG, PSNR improved by up to 28.52% and SSIM by 1.51%, with a compression ratio increase from 14.52 to 27.56. The ROIE-UC also provides up to a 49.44% increase in PSNR over other methods. These results demonstrate that ROIE-UC is an efficient and effective solution for image processing and data compression in UAV applications.

关键词： image segmentation Visualization image coding PSNR Wireless networks Bandwidth Autonomous aerial vehicles Real-time systems image reconstruction Videos

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Per-Pixel Solution of Multispectral Photometric Stereo

Per-Pixel Solution of Multispectral Photometric Stereo

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Shin Ishihara Imari Sato National Institute of Informatics Chiyoda-ku Tokyo

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Photometric Stereo (PS) estimates surface normals by analyzing images lit from different angles. Enhancing PS with spectral imaging, known as multispectral photometric stereo (MPS), uses varying light source colors for simultaneous image capture. As in traditional PS, obtaining a unique solution is challenging in MPS when the reflectance properties of the object are unknown. This paper presents an approach utilizing the spatial arrangement and color of light sources to solve the MPS problem in the condition of spatially varying reflectance from a minimum of seven spectral images without spatial smoothness constraints. A robust optimization technique is introduced to manage real data. Experiments on synthetic and real scenes validate the method's effectiveness, including for non-Lambertian surfaces. The method can contribute to advanced digital archiving that simultaneously records surface normal and spectral reflectance.

关键词： Reflectivity Surface reconstruction Three-dimensional displays Shape image color analysis Surface waves Linearity Real-time systems Spectral analysis Light sources

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Dense Scene reconstruction from Light-Field images Affected by Rolling Shutter

Dense Scene Reconstruction from Light-Field Images Affected ...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Hermes McGriff Renato Martins Nicolas Andreff Cédric Demonceaux CNRS UMR 6303 ICB Université de Bourgogne CNRS UMR 6174 FEMTO-ST Université de Franche-Comté CNRS Inria LORIA Université de Lorraine

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

This paper presents a dense depth estimation approach from light-field (LF) images that is able to compensate for strong rolling shutter (RS) effects. Our method estimates RS compensated views and dense RS compensated disparity maps. We present a two-stage method based on a 2D Gaussians Splatting that allows for a “render and compare” strategy with a point cloud formulation. In the first stage, a subset of sub-aperture images is used to estimate an RS agnostic 3D shape that is related to the scene target shape “up to a motion”. In the second stage, the deformation of the 3D shape is computed by estimating an admissible camera motion. We demonstrate the effectiveness and advantages of this approach through several experiments conducted for different scenes and types of motions. Due to lack of suitable datasets for evaluation, we also present a new carefully designed synthetic dataset of RS LF images. The source code, trained models and dataset will be made publicly available at: https://***/ICB-Vision-AI/DenseRSLF.

关键词： Point cloud compression Adaptation models Three-dimensional displays Shape Deformation Source coding reconstruction algorithms Cameras image reconstruction Synthetic data

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VHS: High-Resolution Iterative Stereo Matching with Visual Hull Priors

VHS: High-Resolution Iterative Stereo Matching with Visual H...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Markus Plack Hannah Dröge Leif Van Holland Matthias B. Hullin University of Bonn Bonn Germany

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

We present a stereo-matching method for depth estimation from high-resolution images using visual hulls as priors, and a memory-efficient technique for the correlation computation. Our method uses object masks extracted from supplementary views of the scene to guide the disparity estimation, effectively reducing the search space for matches. This approach is specifically tailored to stereo rigs in volumetric capture systems, where an accurate depth plays a key role in the downstream reconstruction task. To enable training and regression at high resolutions targeted by recent systems, our approach extends a sparse correlation computation into a hybrid sparse-dense scheme suitable for application in leading recurrent network architectures. We evaluate the performance-efficiency tradeoff of our method compared to state-of-the-art approaches and demonstrate the efficacy of the visual hull guidance. In addition, we propose a training scheme for a further reduction of memory requirements during optimization, facilitating training on high-resolution data.

关键词： Training Visualization Correlation Depth measurement Memory management Estimation Search problems Iterative methods Optimization image reconstruction

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