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

共 14698 条记录，以下是61-70 订阅

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Enduring Memory Self-learning Multi-level Transformer Network for Remote Sensing image Super-Resolution 27th

Enduring Memory Self-learning Multi-level Transformer Networ...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Li, Peishan Zhang, Yonghong Wang, Junfei Ma, Guangyi Nanjing University of Information Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9783031784972

High-resolution (HR) remote sensing is essential for remote sensing image interpretation, but challenges in super-resolution (SR) stem from scale and texture differences within images, neglecting high-dimensional detail extraction and long-range dependencies among multi-dimensional features. Addressing this, we propose TDSNet, a enduring memory self-learning multi-level Transformer Network for remote sensing image super-resolution (RISSR). The utilization of the similarity balancing module and memory gated group establishes connections between mixed-scale information, while also possessing enduring memory across various receptive fields. Shallow and deep-level data fuse in the transformer, employing a dual learning strategy, enhancing reconstruction through a constrained mapping process with a loss function. This transforms the ill-posed problem into a well-posed one. Attribution analysis with the LAM method reveals TDSNet’s efficacy in capturing content information. Experiments on NWPU-RESISC45 and AID datasets demonstrate TDSNet’s superior performance in remote sensing image super-resolution compared to other methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image texture

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7th International Workshop on Predictive Intelligence in Medicine, PRIME 2024, held in conjunction with the 27th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

7th International Workshop on Predictive Intelligence in Med...

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7th International Workshop on Predictive Intelligence in Medicine, PRIME 2024, held in conjunction with the 27th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031745607

The proceedings contain 17 papers. The special focus in this conference is on Predictive Intelligence in Medicine. The topics include: Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive Models for Neuroimaging;RCT: Relational Connectivity Transformer for Enhanced Prediction of Absolute and Residual Intelligence;gene-to-image: Decoding Brain images from Genetics via Latent Diffusion Models;physics-Guided Multi-view Graph Neural Network for Schizophrenia Classification via Structural-Functional Coupling;automated Patient-Specific Pneumoperitoneum Model reconstruction for Surgical Navigation Systems in Distal Gastrectomy;MNA-net: Multimodal Neuroimaging Attention-Based Architecture for Cognitive Decline Prediction;Improving Brain MRI Segmentation with Multi-Stage Deep Domain Unlearning;DynGNN: Dynamic Memory-Enhanced Generative GNNs for Predicting Temporal Brain Connectivity;Strongly Topology-Preserving GNNs for Brain Graph Super-Resolution;generative Hypergraph Neural Network for Multiview Brain Connectivity Fusion;identifying Brain Ageing Trajectories Using Variational Autoencoders with Regression Model in Neuroimaging data Stratified by Sex and Validated Against Dementia-Related Risk Factors;integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction;self-Supervised Contrastive Learning for Consistent Few-Shot image Representations;Neurocognitive Latent Space Regularization for Multi-label Diagnosis from MRI;Segmentation of Brain Metastases in MRI: A Two-Stage Deep Learning Approach with Modality Impact Study.

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image reconstruction based on Graph structure for MRI-guided diffuse optical tomography 17

Image reconstruction based on Graph structure for MRI-guided...

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17th International conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Zou, Peiwen Wei, Chengpu Hu, Ting Li, Zhe Sun, Zhonghua Jia, Kebin Feng, Jinchao Beijing Key Laboratory of Computational Intelligence and Intelligent System School of Information Science and Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

Diffuse optical tomography (DOT) is a non-invasive, label-free imaging technique widely used in applications such as breast cancer diagnosis and brain imaging. It allows for the quantitative measurement of tissue functional parameters, including the concentrations of oxyhemoglobin, deoxyhemoglobin, and water. However, the quality of reconstructed images is poor due to light scattering. To address this challenge, one effective strategy is to incorporate anatomical information from high-resolution imaging to guide DOT reconstruction. In this study, a new approach (GRI) is developed to leverage MRI images based on graph structure for DOT reconstruction. And its feasibility and effectiveness were evaluated with numerical simulations. The results indicate that GRI significantly improves the structural similarity index (SSIM) of reconstructed total hemoglobin (HbT) images, outperforming conventional Tikhonov regularization and direct regularization imaging (DRI) method by more than 63% and 82%, respectively. These findings are further supported by experiments using real patient data, which demonstrate GRI's potential in breast cancer diagnosis. © 2025 SPIE.

关键词： Optical tomography

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Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method

Learning Diffusion Model from Noisy Measurement using Princi...

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2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bai, Weimin Tang, Weiheng Ye, Enze Chen, Siyi Chen, Wenzheng Sun, He Academy for Advanced Studies Peking University China School of Physics Peking University China College of Future Technology Peking University China Wangxuan Institute of Computer Technology Peking University China

ISBN: (纸本)9798350368741

Diffusion models have demonstrated exceptional ability in modeling complex image distributions, making them versatile plug-and-play priors for solving imaging inverse problems. However, their reliance on large-scale clean datasets for training limits their applicability in scenarios where acquiring clean data is costly or impractical. Recent approaches have attempted to learn diffusion models directly from corrupted measurements, but these methods either lack theoretical convergence guarantees or are restricted to specific types of data corruption. In this paper, we propose a principled expectation-maximization (EM) framework that iteratively learns diffusion models from noisy data with arbitrary corruption types. Our framework employs a plug-and-play Monte Carlo method to accurately estimate clean images from noisy measurements, followed by training the diffusion model using the reconstructed images. This process alternates between estimation and training until convergence. We evaluate the performance of our method across various imaging tasks, including inpainting, denoising, and deblurring. Experimental results demonstrate that our approach enables the learning of high-fidelity diffusion priors from noisy data, significantly enhancing reconstruction quality in imaging inverse problems. © 2025 IEEE.

关键词： Bayesian inference Computational imaging diffusion models image priors inverse problems

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An Optimal Rapid reconstruction Algorithm of Stereo Lithography Files 15th

An Optimal Rapid Reconstruction Algorithm of Stereo Lithogra...

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15th China Academic conference on Printing and Packaging, CACPP 2024

作者： Dong, Guirong Zhang, Yuan Feng, Yuxiang Liu, Dianzi Zhou, Shisheng Faculty of Printing Packaging Engineering and Digital Media Technology Xi’an University of Technology Shaanxi China Faculty of Engineering University of East Anglia Norwich United Kingdom

ISBN: (纸本)9789819644667

STL (stereo lithography) files are widely used model files in 3D printing field. However, when 3D model transform into STL file, not only the data redundancy and topology relation loss will occur, but also restricts further optimization and application of STL mesh models. This paper focus on the problem of the storage process of STL files in ASCII format, a fast topological reconstruction algorithm for STL files based on hash tables is proposed. This algorithm uses the modulo division method to construct a hash function, the chain address method is also used to deals with hash collision, then, point and face tables are combined to store the index value relationship between points and faces. Finally, the topological reconstruction of the STL file is achieved. The experimental results show that the reconstruction algorithm proposed can effectively removes redundant data for models of different levels of complexity. After removing redundancy, the average proportion of vertices is only 16.64%, and the average reduction in STL files storage space is 91.23%. This algorithm also balances the relationship between the space utilization rate of the topological structure and the algorithm time complexity, providing convenience for the fast layered processing of STL files. In addition, the time consumption of topological reconstruction based on four different hash functions are analyzed. The results show that the hash function designed in this paper can reduce the computation time significantly, especially dealing with complex model. In conclusion, the method proposed in this paper has excellent efficiency and stability in dealing with models of different complexity levels, and also provides optimization solutions for the next step of manufacturing printing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stereo image processing

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Supplementary Material AnonyNoise: Anonymizing Event data with Smart Noise to Outsmart Re-Identification and Preserve Privacy

Supplementary Material AnonyNoise: Anonymizing Event Data wi...

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2025 IEEE/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Bendig, Katharina Schuster, René Thiemer, Nicole Joisten, Karen Stricker, Didier DFKI- German Research Center for Artificial Intelligence Germany Rptu - University of Kaiserslautern-Landau Germany

ISBN: (纸本)9798331510831

In this supplementary material, we provide a more detailed overview of AnonyNoise, a method developed for predicting data-dependent noise aimed at preventing reidentification. The document is structured as follows: First, we detail the training parameters used in our implementation for the three datasets: DVS-Gesture [4], SEE [29], and Event-ReId [1], in order to ensure reproducibility. Next, we present numerical results from an inversion attack on our method, comparing its effectiveness to Gaussian noise when evaluated using a denoising network. This comparison provides insight into the robustness of AnonyNoise in contrast to traditional noise techniques in preventing data recovery and re-identification attempts. We moreover provide our statement regarding our responsibility to human subjects in the datasets used during our experiments. Lastly, we include an expanded set of visual examples across all datasets, including the results from image reconstruction attacks. © 2025 IEEE.

关键词： data privacy event data re-identification

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Subsampling Decomposition based k-Space Refinement for Accelerated MRI reconstruction

Subsampling Decomposition based k-Space Refinement for Accel...

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2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Qiao, Xiaoyu Li, Weisheng Xiao, Bin Huang, Yuping Yang, Lijian Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Ministry of Education Chongqing China

ISBN: (纸本)9798350368741

In accelerated MRI reconstruction problem, directly recovering all the missing k-space data from undersampled measurements is highly ill-posed and often leads to suboptimal performance. To address the problem, we propose a novel deep unfolding network (DUN) with subsampling decomposition (SD) based k-space refinement to mitigate the ill-posedness. Our method employs a parallel network architecture with a primary branch unfolded by gradient descent-inspired optimization process (GD-PB) for reconstruction. Additionally, we introduce an SD-based auxiliary branch (SD-AB) that decompose the inverse problem into moderately corrupted subproblems. We design a novel subsampling mask predictor that captures both global and local spatial correlations in k-space, ensuring the SD-AB effectively preserves the most well-reconstructed subsets as reliable region (RR). The RR in SD-AB is used to periodically refine the intermediate outputs of the GD-PB, achieving improved accuracy. Experimental results reveal that our method significantly outperforms conventional and SD-based DUN techniques, achieving superior PSNR and SSIM results compared with cutting-edge methods. © 2025 IEEE.

关键词： compressed sensing deep unfolded network MRI reconstruction parallel imaging subsampling decomposition

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EvaSR: Rethinking Efficient Visual Attention Design for image Super-Resolution

EvaSR: Rethinking Efficient Visual Attention Design for Imag...

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2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wu, Zhijian Zhang, Chenhan Huang, Dingjiang School of Data Science and Engineering East China Normal University Shanghai China Trenton Collegiate Institute ON Canada

ISBN: (纸本)9798350368741

Due to the advantages of long-range modeling via the self-attention mechanism, Transformer has taken various vision tasks by storm, including image super-resolution (SR). In this study, we reveal that the convolutional neural network (CNN) with proper visual attention is a more simple and effective paradigm than Transformer in image SR tasks. We reexamine the successful SR models and discover several key characteristics that contribute to accurate image reconstruction. Built on this recipe, we propose a pure CNN-based SR network using efficient visual attention, dubbed EvaSR. Benefiting from the carefully designed visual attention, our EvaSR can favorably capture both local structure and long-range dependencies, and achieve adaptivity in spatial and channel dimensions while retaining the simplicity and efficiency of CNNs. The experimental results demonstrate that our EvaSR achieves state-of-the-art performance among the existing efficient SR methods. Especially, the tiny version of EvaSR needs 21.4% and 15.2% parameters of IMDN and SMSR with better performance. © 2025 IEEE.

关键词： Efficient Network Gated Linear Unit image Super-resolution Transformer

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A Pose-Aware Auto-Augmentation Framework for 3D Human Pose and Shape Estimation from Partial Point Clouds 7th

A Pose-Aware Auto-Augmentation Framework for 3D Human Pose a...

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7th Chinese conference on Pattern Recognition and Computer Vision

作者： Wang, Kangkan Yin, Sliihao Fang, Chenghao Nanjing Univ Sci & Technol Key Lab Intelligent Percept & Syst High Dimens In Minist Educ Nanjing Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Jiangsu Key Lab Image & Video Understanding Socia Nanjing Peoples R China

ISBN: (纸本)9789819785070;9789819785087

This work mainly addresses the challenges in 3D human pose and shape estimation from real partial point clouds. Existing 3D human estimation methods from point clouds usually have limited generalization ability on real data due to factors such as self-occlusion and random noise and domain gap between real data and synthetic data. In this paper, we propose a pose-aware auto-augmentation framework for 3D human pose and shape estimation from partial point clouds. Specifically, we design an occlusion-aware module for the estimator network that can obtain refined features to accurately regress human pose and shape parameters from partial point clouds, even if the point clouds are self-occlusive. Based on the pose parameters and global features of the point clouds from estimator network, we carefully design a learnable augmentor network that can intelligently drive and deform real data to enrich data diversity during the training of estimator network. To guide the augmentor network to generate challenging augmented samples, we adopt an adversarial learning strategy according to the error feedback of the estimator. The experimental results on real data and synthetic data demonstrate that the proposed approach can accurately estimate the 3D human pose and shape from partial point clouds and outperform prior works in terms of reconstruction accuracy.

关键词： 3D human pose and shape estimation Pose-aware auto-augmentation Occlusion-aware network Partial point clouds Real data

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PotC2Vox: A Point Cloud data-Driven 3D reconstruction Method for Single-View images 27th

PotC2Vox: A Point Cloud Data-Driven 3D Reconstruction Method...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Sheng, Jianqiang Chen, Siwei Wang, Fei Zhao, Yongsheng Zhang, Zhineng Jiang, Kai Pan, Xunan Yan, Jingwen Shenzhen Institute of Information Technology Shenzhen China Anhui Agricultural University Hefei China School of Mathematics and Computer Science Shantou University Shantou China

ISBN: (纸本)9783031781858

Generating three-dimensional models from two-dimensional free-hand sketches presents a viable avenue for 3D reconstruction. As a convenient method for 3D reconstruction, sketch-based modeling not only significantly eases the challenges associated with 3D model generation, but also affords designers the flexibility to craft models in alignment with their own conceptual visions. Nevertheless, the intrinsic abstraction and diversity of free-hand sketches, which often lack the rigorous geometric and physical constraints typical of actual images, exacerbate the difficulty in adapting models to various sketch styles. This has resulted in an underexplored methodology within the field. In this study, we introduce PotC2Vox—a novel neural network architecture—whereby a two-dimensional point cloud, derived from preprocessed sketches, is supplied to the network. The network adeptly captures geometric features through dedicated processing modules, utilizing the 2D point cloud to extract image characteristics and to execute the 3D reconstruction. Furthermore, we integrate a Contextual Modal Enhancement Module (CME) into our network, establishing a link between the input features at the expense of minimal network depth, thereby further refining the 3D reconstruction outcomes. Comparative evaluations with a suite of experiments demonstrate that our PotC2Vox model exhibits superior performance in the reconstruction quality and fidelity of 3D models relative to analogous networks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3D reconstruction

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