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

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Scatter correction using FBP with data rearrangement for a single full scan CBCT 2

Scatter correction using FBP with data rearrangement for a s...

引用

2022 2nd International conference on Electronics, Circuits and Information Engineering, ECIE 2022

作者： Kang, Guanyu Han, Yu Xi, Xiaoqi Li, Lei Tan, Siyu Zhao, Hongfeng Yan, Bin Cyberspace Security Institute of Zhengzhou University Zheng zhou He nan China School of Information Systems Engineering The University of Information Engineering Zheng zhou He nan China Dongguan Xinda Optoelectronics Technology Co Guang dong Dong guan China

A scattering plate consisting of multiple lead strips is an effective tool for scatter correction and dose reduction in cone-beam CT(CBCT). Existing scattering plate correction methods take advantage of data redundancy by partially blocking cone beam data from a single scan to make up and perform scattering correction. However, for geometric objects with large cone angles during CBCT scans, existing backprojection-filteration(BPF) algorithms cannot accurately reconstruct the object due to data inconsistency and truncation, and may produce additional image artifacts. In order to improve the image quality at large cone angles, this paper proposes a hybrid scatter correction method for filteration-backprojection (FBP) image reconstruction from partially blocked cone beam data using data rearrangement. The proposed method uses a blocker consisting of multiple lead strips that are placed between the light source and the object to provide scatter correction and image reconstruction with beam-blocked data. In combination with a data rearrangement algorithm, the cone-beam projection data is rearranged into a tilted parallel projection data, followed by weighted filtering and inverse projection reconstruction. The experimental results of CBCT show that the proposed method effectively corrects the scattering in each projection and reconstructs the scatter-corrected image from a single scan. © Published under licence by IOP Publishing Ltd.

关键词： image reconstruction

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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...

引用

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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Facial image reconstruction from Functional Magnetic Resonance Imaging via GAN Inversion with Improved Attribute Consistency

Facial Image Reconstruction from Functional Magnetic Resonan...

引用

IEEE International conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Chang, Pei-Chun Tien, Yan-Yu Chen, Chia-Lin Chen, Li-Fen Chen, Yong-Sheng Chan, Hui-Ling Natl Yang Ming Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Inst Brain Sci Taipei Taiwan Hiroshima Univ Ctr Brain Mind & KANSEI Sci Res Hiroshima Japan

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Neuroscience studies have revealed that the brain encodes visual content and embeds information in neural activity. Recently, deep learning techniques have facilitated attempts to address visual reconstructions by mapping brain activity to image stimuli using generative adversarial networks (GANs). However, none of these studies have considered the semantic meaning of latent code in image space. Omitting semantic information could potentially limit the performance. In this study, we propose a new framework to reconstruct facial images from functional Magnetic Resonance Imaging (fMRI) data. With this framework, the GAN inversion is first applied to train an image encoder to extract latent codes in image space, which are then bridged to fMRI data using linear transformation. Following the attributes identified from fMRI data using an attribute classifier, the direction in which to manipulate attributes is decided and the attribute manipulator adjusts the latent code to improve the consistency between the seen image and the reconstructed image. Our experimental results suggest that the proposed framework accomplishes two goals: (1) reconstructing clear facial images from fMRI data and (2) maintaining the consistency of semantic characteristics.

关键词： fMRI facial image reconstruction GAN inversion attribute manipulation

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MULTI-MODAL MACHINE LEARNING FOR VEHICLE RATING PREDICTIONS USING image, TEXT, AND PARAMETRIC data

MULTI-MODAL MACHINE LEARNING FOR VEHICLE RATING PREDICTIONS ...

引用

ASME International Design Engineering Technical conferences / 43rd Annual Computers and Information in Engineering conference (IDETC-CIE)

作者： Su, Hanqi Song, Binyang Ahmed, Faez MIT Dept Mech Engn Cambridge MA 02139 USA

ISBN: (纸本)9780791887295

Accurate vehicle rating prediction can facilitate designing and configuring good vehicles. This prediction allows vehicle designers and manufacturers to optimize and improve their designs in a timely manner, enhance their product performance, and effectively attract consumers. However, most of the existing data-driven methods rely on data from a single mode, e.g., text, image, or parametric data, which results in a limited and incomplete exploration of the available information. These methods lack comprehensive analyses and exploration of data from multiple modes, which probably leads to inaccurate conclusions and hinders progress in this field. To overcome this limitation, we propose a multi-modal learning model for more comprehensive and accurate vehicle rating predictions. Specifically, the model simultaneously learns features from the parametric specifications, text descriptions, and images of vehicles to predict five vehicle rating scores, including the total score, critics score, performance score, safety score, and interior score. We compare the multi-modal learning model to the corresponding unimodal models and find that the multi-modal model's explanatory power is 4% - 12% higher than that of the unimodal models. On this basis, we conduct sensitivity analyses using SHAP to interpret our model and provide design and optimization directions to designers and manufacturers. Our study underscores the importance of the data-driven multi-modal learning approach for vehicle design, evaluation, and optimization. We have made the code publicly available at http:// ***/ projects/ vehicleratings/.

关键词： Multi-modal Learning Machine Learning Vehicle Rating Prediction Model Interpretability Sensitivity Analytsis

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Reconstructing Hands in 3D with Transformers

Reconstructing Hands in 3D with Transformers

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

作者： Pavlakos, Georgios Shan, Dandan Radosavovic, Ilija Kanazawa, Angjoo Fouhey, David Malik, Jitendra UT Austin Austin TX 78712 USA Univ Michigan Ann Arbor MI 48109 USA Univ Calif Berkeley Berkeley CA USA NYU New York NY USA

ISBN: (纸本)9798350353006

We present an approach that can reconstruct hands in 3D from monocular input. Our approach for Hand Mesh Recovery, HaMeR, follows a fully transformer-based architecture and can analyze hands with significantly increased accuracy and robustness compared to previous work. The key to HaMeR's success lies in scaling up both the data used for training and the capacity of the deep network for hand reconstruction. For training data, we combine multiple datasets that contain 2D or 3D hand annotations. For the deep model, we use a large scale Vision Transformer architecture. Our final model consistently outperforms the previous baselines on popular 3D hand pose benchmarks. To further evaluate the effect of our design in non-controlled settings, we annotate existing in-the-wild datasets with 2D hand keypoint annotations. On this newly collected dataset of annotations, IIInt, we demonstrate significant improvements over existing baselines. We will make our code, data and models publicly available upon publication. We make our code, data and models available on the project website: https://***/hamer/.

关键词： 3D hand pose estimation hand mesh recovery single-image 3D

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Segment-Based, User-Generated image Styling with Neural Style Transfer 2

Segment-Based, User-Generated Image Styling with Neural Styl...

引用

2nd IEEE International conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Krishna, C. Vamshi Venkatesh, Karthik Nibin, Sooraj Vishakh, H. Nair, Manjula G. Amrita Vishwa Vidhyapeetam Department of Electrical and Electronics Engineering Amritapuri India

ISBN: (纸本)9798350347456

With the recent improvement in machine learning and deep learning technologies due to an increase in computation power, its uses for image processing and computer vision have also increased. The style of the image, through orthodox methods, has been changed with the help of filters that are not capable of applying the style from only a few sets of options, with no consideration for semantic or contextual data. The transfer of style is also limited to the entire image, with no provisions for applying style to only a selected portion of it. Through advanced deep learning technologies, style transfer can be achieved with semantic and contextual accuracy while also providing the ability to apply to a selected portion of the image. The style can also be generated through the use of images generated for the text using deep learning. © 2023 IEEE.

关键词： image reconstruction

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Learning to Follow Frequency View Guidance for Dental CT images Deblurring

Learning to Follow Frequency View Guidance for Dental CT Ima...

引用

2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ding, Jiangang Du, Yiquan Shan, Yihui Pei, Lili Li, Wei Chang'an University School of Information Engineering Xi'an China Chang'an University School of Data Science and Artificial Intelligence Xi'an China

ISBN: (纸本)9798350386226

Factors such as motion blur and scattering noise continuously degrade dental computed tomography images in unpredictable ways. This makes existing natural image-based restoration methods insufficient to tackle this challenge. This work proposes a novel super-resolution pipeline aimed at comprehensively and holistically reconstructing the true pixel-level details of DCTI, named DCT-Diff. Specifically, we formulate DCTI reconstruction as a denoising diffusion process. Using a two-stage pre-training strategy, we create a compact high-frequency prior for the reconstruction process and achieve high-quality reconstruction through a regression-based approach. During the feature extraction and refinement process, we emphasize separating frequency domain representations from ground truth values. This helps suppress ringing and overshoot artifacts generated during imaging. Our method is simple, robust, and demonstrates State-Of-The-Art (SOTA) visual quality and quantitative metrics compared to 12 frameworks in benchmark evaluations. © 2024 IEEE.

关键词： Computerized tomography

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Detection of Brain Tumor using Transfer Learning using Conventional Autoencoder with Long Short Term Memory Method 6

Detection of Brain Tumor using Transfer Learning using Conve...

引用

6th International conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Agalya, D. Kamalakkannan, S. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798331522667

Brain tumor (BT) has generate a significant health challenge by putting pressure on healthy brain parts or spreading into other areas as well as blocking the flow of fluid around the brain. BT diagnosis is an extensive and time consumption process that depends primarily on radiologists experience and interpretive capabilities. For the area of medical imaging, automatic segmentation of images by means of Deep Learning (DL) approaches. The DL approaches assist in transforming the sector, resulted in improving precision and effectiveness of investigations. However, there are various feature extraction mechanisms available. Therefore, the Recurrent Neural Network (RNN) is utilized for feature extraction and image classification. In model execution, the data have trained in transforming the test image as well as data features for minimizing the domain shift is calculated through the Convolutional Autoencoder (CAE) for reconstruction loss. This research has concentrated in building a model with VGG16 as a single test that subjected at inference and existing method is adopted as neural networks for AE as Transfer Learning (TL) that performs an image analysis task such as segmentation and even set as an adopter for pre training the model. The AE used to train from the source dataset and perform as the adaptors in optimizing during testing using a test subject for effective computation. Moreover, the Long Short Term Memory (LSTM) is utilized as RNN model with CAE for providing improved detection of BT in healthcare industries. Hence, the proposed CAE with LSTM is compared with AE with Convolutional Neural Network (CNN) for evaluating BT detection using MRI dataset with various BT type classifications. © 2025 IEEE.

关键词： image segmentation

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Focus on Content not Noise: Improving image Generation for Nuclei Segmentation by Suppressing Steganography in CycleGAN

Focus on Content not Noise: Improving Image Generation for N...

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IEEE/CVF International conference on Computer Vision (ICCV)

作者： Utz, Jonas Weise, Tobias Schlereth, Maja Wagner, Fabian Thies, Mareike Gu, Mingxuan Uderhardt, Stefan Breininger, Katharina Friedrich Alexander Univ Erlangen Nuremberg Dept Artificial Intelligence Biomed Engn Erlangen Germany FAU Erlangen Nurnberg Pattern Recognit Lab Erlangen Germany FAU Erlangen Nurnberg Dept Med Rheumatol & Immunol 3 Erlangen Germany FAU Erlangen Nurnberg Deutsch Zentrum Immuntherapie Erlangen Germany FAU Erlangen Nurnberg Exploratory Res Unit Opt Imaging Ctr Erlangen Erlangen Germany

ISBN: (纸本)9798350307443

Annotating nuclei in microscopy images for the training of neural networks is a laborious task that requires expert knowledge and suffers from inter- and intra-rater variability, especially in fluorescence microscopy. Generative networks such as CycleGAN can inverse the process and generate synthetic microscopy images for a given mask, thereby building a synthetic dataset. However, past works report content inconsistencies between the mask and generated image, partially due to CycleGAN minimizing its loss by hiding shortcut information for the image reconstruction in high frequencies rather than encoding the desired image content and learning the target task. In this work, we propose to remove the hidden shortcut information, called steganography, from generated images by employing a low pass filtering based on the discrete cosine transform (DCT). We show that this increases coherence between generated images and cycled masks and evaluate synthetic datasets on a downstream nuclei segmentation task. Here we achieve an improvement of 5.4 percentage points in the F1-score compared to a vanilla CycleGAN. Integrating advanced regularization techniques into the CycleGAN architecture may help mitigate steganography-related issues and produce more accurate synthetic datasets for nuclei segmentation.

关键词： cycle gan data generation dct fluorescence microscopy nuclei segmentation steganography synthetic data

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RAFaRe: Learning Robust and Accurate Non-parametric 3D Face reconstruction from Pseudo 2D&3D Pairs 37

RAFaRe: Learning Robust and Accurate Non-parametric 3D Face ...

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37th AAAI conference on Artificial Intelligence (AAAI) / 35th conference on Innovative Applications of Artificial Intelligence / 13th Symposium on Educational Advances in Artificial Intelligence

作者： Guo, Longwei Zhu, Hao Lu, Yuanxun Wu, Menghua Cao, Xun Nanjing Univ Nanjing Peoples R China

ISBN: (纸本)9781577358800

We propose a robust and accurate non-parametric method for single-view 3D face reconstruction (SVFR). While tremendous efforts have been devoted to parametric SVFR, a visible gap still lies between the result 3D shape and the ground truth. We believe there are two major obstacles: 1) the representation of the parametric model is limited to a certain face database;2) 2D images and 3D shapes in the fitted datasets are distinctly misaligned. To resolve these issues, a large-scale pseudo 2D&3D dataset is created by first rendering the detailed 3D faces, then swapping the face in the wild images with the rendered face. These pseudo 2D&3D pairs are created from publicly available datasets which eliminate the gaps between 2D and 3D data while covering diverse appearances, poses, scenes, and illumination. We further propose a non-parametric scheme to learn a well-generalized SVFR model from the created dataset, and the proposed hierarchical signed distance function turns out to be effective in predicting middle-scale and small-scale 3D facial geometry. Our model outperforms previous methods on FaceScape-wild/lab and MICC benchmarks and is well generalized to various appearances, poses, expressions, and in-the-wild environments. The code is released at https://***/zhuhao-nju/rafare.

关键词： image reconstruction

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