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

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A Novel Technique for Converting images from 2D to 3D using Deep Neural Networks

A Novel Technique for Converting Images from 2D to 3D using ...

引用

2023 IEEE International conference on Innovative data Communication Technologies and Application, ICIDCA 2023

作者： Kulkarni, Vikrant Subhash Trivedi, Rishikesh Bhupendra Goyal, Somya Rakesh Manipal Univeristy Jaipur Computer and Communication Engineering Jaipur India

ISBN: (纸本)9798350397208

The conversion of 2D images into 3D images has been a crucial scientific problem. In the industries of computer graphics and animations, medical imaging augmented and virtual reality, and others, reconstruction of 3D images from 2D images is high in demand. Researchers deploy convolutional networks for the reconstruction of 3D images. This paper compares the existing techniques and proposes a MeshCNN-based nove1 technique to convert 2D images into 3D images. The publicly available dataset namely VisDrone is used for experimental study. The proposed technique has been validated against the existing techniques. It is concluded that the proposed model has the potential to effectively convert 2D images into 3D images. © 2023 IEEE.

关键词： image segmentation

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QID2: An image-Conditioned Diffusion Model for Q-Space Up-Sampling of DWI data 15th

QID2: An Image-Conditioned Diffusion Model for Q-Space Up-S...

引用

15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Chen, Zijian Wang, Jueqi Venkataraman, Archana Department of Electrical and Computer Engineering Boston University Boston United States

ISBN: (纸本)9783031869198

We propose an image-conditioned diffusion model to estimate high angular resolution diffusion weighted imaging (DWI) from a low angular resolution acquisition. Our model, which we call QID2, takes as input a set of low angular resolution DWI data and uses this information to estimate the DWI data associated with a target gradient direction. We leverage a U-Net architecture with cross-attention to preserve the positional information of the reference images, further guiding the target image generation. We train and evaluate QID2 on single-shell DWI samples curated from the Human Connectome Project (HCP) dataset. Specifically, we sub-sample the HCP gradient directions to produce low angular resolution DWI data and train QID2 to reconstruct the missing high angular resolution samples. We compare QID2 with two state-of-the-art GAN models. Our results demonstrate that QID2 not only achieves higher-quality generated images, but it consistently outperforms state-of-the-art baseline methods in downstream tensor estimation across multiple metrics and in generalizing to downsampling scenario during testing. Taken together, this study highlights the potential of diffusion models, and QID2 in particular, for q-space up-sampling, thus offering a promising toolkit for clinical and research applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep Learning Diffusion Models Diffusion Weighted Imaging Q-Space Up-sampling Tensor reconstruction

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Advancing Healthcare: Innovative ML Approaches for Improved Medical Imaging in data-Constrained Environments

Advancing Healthcare: Innovative ML Approaches for Improved ...

引用

2024 IEEE Global Communications conference, GLOBECOM 2024

作者： Amin, Al Hasan, Kamrul Zein-Sabatto, Saleh Hong, Liang Shetty, Sachin Ahmed, Imtiaz Islam, Tariqul Tennessee State University NashvilleTN United States Old Dominion University NorfolkVA United States Howard University WashingtonDC United States Syracuse University SyracuseNY United States

ISBN: (纸本)9798350351255

Healthcare industries face challenges when experiencing rare diseases due to limited samples. Artificial Intelligence (AI) communities overcome this situation to create synthetic data which is an ethical and privacy issue in the medical domain. This research introduces the CAT-U-Net framework as a new approach to overcome these limitations, which enhances feature extraction from medical images without the need for large datasets. The proposed framework adds an extra concatenation layer with downsampling parts, thereby improving its ability to learn from limited data while maintaining patient privacy. To validate, the proposed framework's robustness, different medical conditioning datasets were utilized including COVID-19, brain tumors, and wrist fractures. The framework achieved nearly 98% reconstruction accuracy, with a Dice coefficient close to 0.946. The proposed CAT-U-Net has the potential to make a big difference in medical image diagnostics in settings with limited data. © 2024 IEEE.

关键词： Differential privacy

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SIUNet: Sparsity Invariant U-Net for Edge-Aware Depth Completion 23

SIUNet: Sparsity Invariant U-Net for Edge-Aware Depth Comple...

引用

23rd IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Ramesh, Avinash Nittur Giovanneschi, Fabio Gonzalez-Huici, Maria A. Fraunhofer FHR Wachtberg Germany

ISBN: (纸本)9781665493468

Depth completion is the task of generating dense depth images from sparse depth measurements, e.g., LiDARs. Existing unguided approaches fail to recover dense depth images with sharp object boundaries due to depth bleeding, especially from extremely sparse measurements. State-ofthe-art guided approaches require additional processing for spatial and temporal alignment of multi-modal inputs, and sophisticated architectures for data fusion, making them non-trivial for customized sensor setup. To address these limitations, we propose an unguided approach based on UNet that is invariant to sparsity of inputs. Boundary consistency in reconstruction is explicitly enforced through auxiliary learning on a synthetic dataset with dense depth and depth contour images as targets, followed by fine-tuning on a real-world dataset. With our network architecture and simple implementation approach, we achieve competitive results among unguided approaches on KITTI benchmark and show that the reconstructed image has sharp boundaries and is robust even towards extremely sparse LiDAR measurements.

关键词： Training Laser radar image edge detection Computer architecture Network architecture Sensor systems Task analysis

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Multi-Source Domain Adaptation with incomplete Source Label Spaces 27

Multi-Source Domain Adaptation with Incomplete Source Label ...

引用

27th International conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Li, Keqiuyin Lu, Jie Zuo, Hua Zhang, Guangquan Australia Artificial Intelligence Institute University of Technology Sydney 81 Broadway Ultimo2007 Australia

Domain adaptation is a practicable tool in real world application where there exists data scarcity. Multi-source domain adaptation attracts increasing attention due to its ability to enrich transfer knowledge by combining information from multiple domains. However, knowledge transfer can trigger privacy concerns by accessing source data. In addition, multiple domains can have label heterogeneity problem. In this paper, to solve the mentioned problems, we conduct an incomplete multi-source domain adaptation (IMSDA) method which can address transfer learning with and without the access to source data. As far as we are aware, this is the first work handling source-free incomplete domain adaptation. To take the benefits of multiple sources, multi-task learning is adopted to learn a general source model which can perform on multiple domains. A data matching strategy with and without source data forcing target sample to source latent feature space is developed to combine with self-supervision to adapt source model to the target domain. Experiments on real-world datasets indicate the superiority of the proposed method. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： domain adaptation image classification machine learning Transfer learning

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Integrating Quantum Computing for Enhanced image reconstruction in Medical Diagnostics

Integrating Quantum Computing for Enhanced Image Reconstruct...

引用

2024 International conference on Communication, Computer Sciences and Engineering, IC3SE 2024

作者： Anuradha, R. Vandana, C.P. Singh, S. Vikram Singh, Navdeep Emad, Rafal Nitin, Vemuri Institute Of Aeronautical Engineering Department Of Information Technology Telangana Hyderabad India New Horizon College Of Engineering Department Of Information Science Engineering Bangalore India Amity University Greater Department Of Electrical & Electronics Engineering Noida India Lovely Professional University Phagwara India Hilla University College Babylon Iraq Mlr Institute Of Technology Department Of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798350366846

The ever-growing field of medical diagnostics is witnessing a paradigm shift with the integration of quantum computing and deep neural networks (DNN). This paper presents a novel approach that harnesses the computational prowess of quantum algorithms combined with the capabilities of DNNs. The core of this research lies in the development of a novel quantum-enhanced neural network model, for the complex task of reconstructing high-resolution medical images from limited or noisy data sets. The proposed model leverages quantum parallelism to significantly improve the training process of deep networks, while maintaining high fidelity in image reconstruction. Extensive experiments demonstrate that this integrated approach outperforms conventional methods in terms of both speed and accuracy. The quantum-DNN framework exhibits remarkable proficiency in handling large-scale medical images, showing substantial improvements in detecting fine-grained features critical for accurate diagnosis. This paper also delves into the optimization of quantum circuits and layer architectures, ensuring minimal qubit requirements while maximizing computational efficiency. © 2024 IEEE.

关键词： Deep neural networks

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A Non-local Low Rank and Total Variation Approach for Depth image Estimation 15

A Non-local Low Rank and Total Variation Approach for Depth ...

引用

15th RIVF International conference on Computing and Communication Technologies (RIVF)

作者： Mau Uyen Nguyen Truong Giang Tong Tat Thang Hoa Dai Duong Ha Van Ha Tang Le Quy Don Tech Univ Fac Informat Technol Hanoi Vietnam

ISBN: (纸本)9781665404358

Accurate depth reconstruction is vital for numerous applications including autonomous vehicles, virtual reality, and robot perception. However, the depth imaging is challenging because of limited hardware operations, resource-constrained limitations, and incomplete data measurements. To address such shortcomings, this paper introduces an imaging model for efficient depth image estimation from incomplete depth pixels using non-local low-rank (NLLR) and total variation (TV) representations. The motivation is that NLLR is used to model global similar structure among depth patches, and the TV is incorporated to capture the correlations among local depth pixels. We reformulate the problem of depth reconstruction as a regularized least squares minimization problem with the non-local LR and TV regularizers. Furthermore, this paper proposes an iterative algorithm using the alternating direction method of multipliers (ADMM) to solve the optimization model, yielding an estimate of the depth map from far reduced data points. Experimental results on benchmark datasets validate the efficiency of the proposed approach.

关键词： Depth imaging RGB-D imaging sparse reconstruction low-rank approximation

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G-NeRF: Geometry-enhanced Novel View Synthesis from Single-View images

G-NeRF: Geometry-enhanced Novel View Synthesis from Single-V...

引用

IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Huang, Zixiong Chen, Qi Sung, Libo Yang, Yifan Wang, Naizhou Wu, Qi Tan, Mingkui South China Univ Technol Guangzhou Peoples R China Univ Adelaide Adelaide SA Australia Guangzhou Shiyuan Elect Co Ltd Guangzhou Peoples R China Pazhou Lab Pazhou Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot Beijing Peoples R China

ISBN: (纸本)9798350353006

Novel view synthesis aims to generate new view images of a given view image collection. Recent attempts address this problem relying on 3D geometry priors (e.g., shapes, sizes, and positions) learned from multi-view images. However, such methods encounter the following limitations: 1) they require a set of multi-view images as training data for a specific scene (e.g., face, car or chair), which is often unavailable in many real-world scenarios;2) they fail to ex-tract the geometry priors from single-view images due to the lack of multi-view supervision. In this paper, we propose a Geometry-enhanced NeRF (G-NeRF), which seeks to enhance the geometry priors by a geometry-guided multi-view synthesis approach, followed by a depth-aware train-ing. In the synthesis process, inspired that existing 3D GAN models can unconditionally synthesize high-fidelity multi-view images, we seek to adopt off-the-shelf 3D GAN models, such as EG3D, as a free source to provide geometry priors through synthesizing multi-view data. Simultaneously, to further improve the geometry quality of the synthetic data, we introduce a truncation method to effectively sample latent codes within 3D GAN models. To tackle the absence of multi-view supervision for single-view images, we design the depth-aware training approach, incorporating a depth-aware discriminator to guide geometry priors through depth maps. Experiments demonstrate the effectiveness of our method in terms of both qualitative and quantitative results.

关键词： NeRF Single-view reconstruction

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image Inpainting on Archeological dataset Using UNet Architecture on Embedded Platform 4th

Image Inpainting on Archeological Dataset Using UNet Archite...

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4th International conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Kulkarni, Uday Chikkamath, Satish Mirajkar, James Samuel Hittalmakki, Yashas Thota, Venkatpavankumar Khan, Fardin School of Computer Science KLE Technological University Hubballi India School of Electronics and Communication KLE Technological University Hubballi India

ISBN: (纸本)9789819994410

image inpainting is a widely used technique for reconstructing damaged or missing portions of images, with applications in image editing, object removal, and image restoration. This paper proposes a novel approach to enhance the quality of image inpainting specifically in archaeological datasets. Our method focuses on enhancing the UNet architecture by incorporating the MS-SSIM loss function, which effectively denoises the output. We conducted experiments using datasets from archaeological sites, which often contain photographs with missing or damaged areas due to the passage of time or excavation procedures. Our results demonstrate that the proposed approach significantly improves the SSIM score, increasing it from 0.9026 to 0.9590. Additionally, we employed post-training quantization, resulting in a remarkable 77.1% reduction in the model size, making it more suitable for deployment in mobile applications. The suggested approach enhances image restoration and analysis for archaeological data, providing academics and archaeologists with a valuable tool for their work. Furthermore, by integrating the model into mobile apps, we aim to expand its usability and accessibility, empowering users to examine and restore their own archaeological images effectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image reconstruction

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Dual-branch reconstruction network for industrial anomaly detection with RGB-D data

Dual-branch reconstruction network for industrial anomaly de...

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2024 International conference on image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Bi, Chenyang Li, Yueyang Luo, Haichi Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China College of Internet of Things Engineering Jiangnan University 1800 Lihu Avenue Jiangsu Wuxi China

ISBN: (纸本)9781510680425

Unsupervised anomaly detection methods are at the forefront of industrial anomaly detection efforts and have made notable progress. Previous work primarily used 2D information as input, while multi-modal industrial anomaly detection based on 3D point clouds and RGB images is just beginning to emerge. The regular approach involves utilizing large pre-trained models for feature representation and storing them in memory banks. However, the above methods require a longer inference time and higher memory usage, which cannot meet the real-time requirements of the industry. To overcome these issues, we propose a lightweight dual-branch reconstruction network(DBRN) based on RGB-D input, learning the decision boundary between normal and abnormal examples. The requirement for alignment between the two modalities is eliminated by using depth maps instead of point cloud input. Furthermore, we introduce an importance scoring module in the discriminative network to assist in fusing features from these two modalities, thereby obtaining a comprehensive discriminative result. DBRN achieves 92.8% AUROC with high inference efficiency on the MVTec 3D-AD dataset without large pre-trained models and memory banks. © 2024 SPIE.

关键词： Self-supervised learning

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