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

共 14747 条记录，以下是321-330 订阅

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Multistatic cylindrical fast backprojection 3D image reconstruction 28

Multistatic cylindrical fast backprojection 3D image reconst...

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conference on Radar Sensor Technology XXVIII

作者： Sheen, David M. Clark, R. Trevor Pacific Northwest Natl Lab US DOE Battelle Mem Inst POB 999 Richland WA 99352 USA

ISBN: (纸本)9781510674158;9781510674141

The Pacific Northwest National Laboratory (PNNL) has recently developed a next-generation cylindrical millimeter-wave imaging system. This system is based on linear sparse multistatic imaging arrays. data from this system can be focused using 3D FFT-based reconstruction algorithms, which are reasonably efficient and can be performed in near real time, or by back-projection methods that are versatile and more accurate but are computationally intensive and require lengthy post-processing. Cylindrical Fast Backprojection (CFBP) is a novel image reconstruction algorithm developed at PNNL that radically increases the efficiency of backprojection and is ideally suited to microwave and millimeter-wave imaging systems based on scanned linear arrays such as body scanners in common use for aviation security screening. This method achieves its gains in efficiency by separating a full backprojection into a sequence of three steps, range focusing, vertical focusing, and lateral focusing, with intermediate results used to avoid repetitive multidimensional computation. The method is called cylindrical fast backprojection due to the use of two-dimensional stored results, or look-up tables, that have cylindrical symmetry about the linear array. The method is well suited to cylindrically scanned linear arrays but is equally valid for linear arrays scanned to form planar or arbitrary apertures. This paper describes the CFBP algorithm and validates its performance using simulated data.

关键词： Millimeter-wave imaging microwave imaging concealed-weapon detection security screening

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RGB-Guided Spectral image Generation through GANs with Implicit Spectral Learning 32

RGB-Guided Spectral Image Generation through GANs with Impli...

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32nd European Signal Processing conference (EUSIPCO)

作者： Martinez, Emmanuel Jacome, Roman Jerez, Andres Arguello, Henry Univ Ind Santander Dept Comp Sci Bucaramanga Colombia Univ Ind Santander Dept Elect Engn Bucaramanga Colombia

ISBN: (纸本)9789464593617;9798331519773

Spectral imaging is a technique that enables the acquisition and analysis of radiation emitted by incident light from a scene. Spectral acquisition involves various scanning strategies, such as snapshot spectral image acquisition based on compressive sensing theory. However, scanning methods require meticulous calibration processes in the optical setups and pose challenges in implementation, especially in uncontrolled environments. Current research has employed generative adversarial networks (GANs) to produce new spectral images and alleviate reliance on complex optical setups. In addition, traditional methods are focused on RGB-to-spectral mapping techniques, where new spectral images are not created. Therefore, this work proposes spectral imaging generation through RGB image dataset guidance by using GANs. Specifically, a generative model can produce spectral images from random noise input and map them to RGB images through a spectral response matrix, which is fed into a discriminator model for adversarial training. To ensure realistic spectral image generation, an implicit learning approach to spectral information is introduced, where a pre-trained model with spectral images is used to regularize the generated spectral images during training. Finally, a post-processing step normalizes the mean and standard deviation of generated spectral images according to each spectral band of the real training spectral image dataset. The generated spectral images are validated as a data augmentation strategy by performing spectral image reconstruction based on compressive sensing and using RGB images.

关键词： Spectral image generation generative adversarial networks RGB images compressive spectral imaging

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Research on 3D Object reconstruction Based on Single-view RGB image

Research on 3D Object Reconstruction Based on Single-view RG...

引用

2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Zheng, Qiuhong Ding, Peng Shen, Yun Wang, Jinghan Zhang, Zhichao China Telecom Corporation Research Institute Institute of Big Data and Artificial Intelligence Beijing China

ISBN: (纸本)9798350321524

With the development of immersive virtual reality technology, how to quickly and conveniently perform three-dimensional (3D) reconstruction of objects has become a hot research topic. In this paper, we propose a method to reconstruct 3D objects from single view RGB images. The depth SDF representation of 3D shape is predicted from the input image, and the shape is expressed in a compact way, which can predict the shape with complex topology and retain the shape details. In view of the lack of prior use of the shape of similar objects in the prior art, which may generate incomplete and unrealistic shapes, an implicit template shape is constructed in this paper, and a coordinate transformation field of the implicit template shape is proposed to be estimated from the input RGB image, and the SDF of the implicit template is transformed into the SDF of the estimated shape. The shape prior contained in the template shape is used, which improves the robustness of the shape estimation. © 2023 IEEE.

关键词： Computer vision

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Extraction of Core data in Electrical Impedance Tomography (EIT)

Extraction of Core Data in Electrical Impedance Tomography (...

引用

IEEE International Instrumentation and Measurement Technology conference (I2MTC) - Rising Above Covid-19

作者： Zhang, Hanyu Li, Nan Northwestern Polytech Univ Adv Intelligent Measurement & Control & Nav Techn Xian Peoples R China Northwestern Polytech Univ Sch Automat Xian Peoples R China

ISBN: (纸本)9781665453837

The inherent nonlinear and ill-posed attributes of Electrical Impedance Tomography (EIT) lead to the poor accuracy of reconstructed images. In order to improve the imaging accuracy, a Multi-Scale Attention Network (MSA-Net) is designed to select core data from voltage measurements for image reconstruction. The MSA-Net is composed of Generation Module (GM), multi scale Convolutional Neural Network (CNN) module and classifier. The proposed method is tested on 8-electrode, 12-electrode and 16-electrode EIT. The de-redundancy rate of the proposed method will reduce with the increase electrodes, but still higher than 40%. The imaging results of LBP and TR algorithms indicates that the artifacts in the 12-electrode and 16-electrode reconstructed images can be greatly reduced by removing redundant data, and the conductivity distribution area of bubbles can be reconstructed more accurately.

关键词： electrical impedance tomography (EIT) image reconstruction deep learning core data

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A New Multimodal and Spatio-Temporal dataset for Traffic Control: Development, Analysis, and Potential Applications

A New Multimodal and Spatio-Temporal Dataset for Traffic Con...

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International conference on Advanced Systems and Emergent Technologies (ICASET)

作者： Godhbani, Salah Elkosantini, Sabeur Lee, Seongkwan M. Suh, Wonho Univ Tunis ISG Tunis Tunisia Univ Carthage Fac Econ & Management Nabeul Tunisia Kunsan Natl Univ Sch Liberal Studies Jeollabuk Do South Korea Hanyang Univ ERICA Campus Seoul South Korea

ISBN: (纸本)9798350384901;9798350384895

Multimodal data, which includes various data formats such as image, video, text, and sensor data, is essential for urban traffic management. The lack of proven multimodal transportation data has been a significant challenge for urban planners, leading to biased or incomplete estimates of travel demand, mode choice, and network performance. Multimodal data integration offers a valuable resource for understanding and optimizing traffic control and management. However, the heterogeneity of the data, various kinds of noise, alignment of modalities, and techniques to handle missing data are some of the challenges that arise. This paper presents a novel multimodal dataset which is the first of its kind, its scraped from England Highways, incorporating speed, flow, and camera images for the M60, M25, and M1 motorways. The dataset provides a comprehensive view of traffic behavior at specific junctions, enabling detailed analysis and real-world applications. By integrating previously disparate data sources, this dataset offers a valuable resource for understanding and optimizing traffic control and management. The paper outlines the dataset's development, including the gathering of speed and flow data, and the use of image scraping techniques to capture CCTV images. The potential applications of the dataset for traffic control, planning, and optimization are also discussed. Overall, this multimodal dataset represents a significant contribution to the field, with implications for the development of advanced traffic management systems and the improvement of transportation infrastructure.

关键词： multimodal dataset traffic control image speed flow Machine learning

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Simulation-Based Parameter Optimization for Fetal Brain MRI Super-Resolution reconstruction 26th

Simulation-Based Parameter Optimization for Fetal Brain MRI ...

引用

26th International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： de Dumast, Priscille Sanchez, Thomas Lajous, Helene Cuadra, Meritxell Bach Lausanne Univ Hosp CHUV Dept Radiol Lausanne Switzerland Univ Lausanne UNIL Lausanne Switzerland CIBM Ctr Biomed Imaging Lausanne Switzerland

ISBN: (纸本)9783031439896;9783031439902

Tuning the regularization hyperparameter a in inverse problems has been a longstanding problem. This is particularly true in the case of fetal brain magnetic resonance imaging, where an isotropic high-resolution volume is reconstructed from motion-corrupted low-resolution series of two-dimensional thick slices. Indeed, the lack of ground truth images makes challenging the adaptation of a to a given setting of interest in a quantitative manner. In this work, we propose a simulationbased approach to tune a for a given acquisition setting. We focus on the influence of the magnetic field strength and availability of input low-resolution images on the ill-posedness of the problem. Our results show that the optimal a, chosen as the one maximizing the similarity with the simulated reference image, significantly improves the super-resolution reconstruction accuracy compared to the generally adopted default regularization values, independently of the selected reconstruction pipeline. Qualitative validation on clinical data confirms the importance of tuning this parameter to the targeted clinical image setting. The simulated data and their reconstructions are available at https://zenodo. org/record/8123677.

关键词： Magnetic resonance imaging (MRI) fetal brain MRI Super-resolution reconstruction (SRR) parameter optimization image synthesis

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Design and Optimization of Swin Transformers-Based image Semantic Communication System under SIMO Channels 12

Design and Optimization of Swin Transformers-Based Image Sem...

引用

12th IEEE International conference on Information and Communication Networks, ICICN 2024

作者： Wang, Jinggang Wang, Junxuan Zhang, Yanyan Jiang, Fan Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an China

ISBN: (纸本)9798350355802

Semantic communication is a new type of communication that improves bandwidth efficiency by transmitting semantic information of data, which can transmit a variety of modalities such as image, text, video, and audio. Semantic models based on convolutional neural networks (CNNs) make it difficult to capture global features resulting in degradation of transmission performance, and the traditional independent source channel coded image transmission method is susceptible to changes in the channel environment. To resolve these problems, this paper proposes a semantic model called SwinJSCC-SIMO, a joint source-channel coding (JSCC) technique using Swin Transformer as the model backbone for image transmission over SIMO channels. Specifically, the SIMO channel sets multiple antennas at the receiving end to receive signals, which improves the reliability of the received signals. The reconstructed image output from the decoder is input to the multiscale discriminator network (DSN) to identify the authenticity of the reconstructed image, guide the semantic model to reconstruct the image. The experimental data show that SwinJSCC-SIMO has better image reconstruction capability, stronger robustness, and signal-to-noise ratio adaptation. © 2024 IEEE.

关键词： Convolutional neural networks

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image Steganography without Embedding Based on Style Transfer and Zero-watermarking 4

Image Steganography without Embedding Based on Style Transfe...

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4th IEEE International conference on data Science and Computer Application, ICDSCA 2024

作者： Sun, Tao Gao, Wenpeng Li, Fanbing School of Information and Automation Engineering Qilu University of Technology Shandong Academy of Sciences Jinan China School of Mechanical and Electronic Engineering Shandong Jianzhu University Jinan China

ISBN: (纸本)9798350368239

Currently, most of the existing steganography approaches are built on the basis of embedding. Regardless of how good the cover picture is chosen or how skillful it is, it will result in a variation of the cover image and arouse the suspect of the attacker. This paper presents a new method of nonembedding image steganography in order to accomplish the secret transport task. Firstly, the sender uses the Quaternion Polar Harmonic Fourier Moments (QPHFM) stabilization properties to carry out the key of the primary image. Then, based on the style transfer network, the format of the original cover image is modified and used to transmit on the common channel. As for the receiver, a special destylization network is adopted in the receiver to reconstruct the raw image. Then, we use a key to get the hidden message from the reconstruction of the original image. In the entire transfer, there is no embedding of the secret message, and it is known as nonembedding image steganography. Moreover, even if the key is broken, it will not be taken away by the attacker. The wide range of experiments show that this method is able to accomplish the security and success of secret transfer. ©2024 IEEE.

关键词： Watermarking

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HiLo: Detailed and Robust 3D Clothed Human reconstruction with High-and Low-Frequency Information of Parametric Models

HiLo: Detailed and Robust 3D Clothed Human Reconstruction wi...

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

作者： Yang, Yifan Liu, Dong Zhang, Shuhai Deng, Zeshuai Huang, Zixiong Tan, Mingkui South China Univ Technol Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot Beijing Peoples R China

ISBN: (纸本)9798350353006

Reconstructing 3D clothed human involves creating a detailed geometry of individuals in clothing, with applications ranging from virtual try-on, movies, to games. To enable practical and widespread applications, recent advances propose to generate a clothed human from an RGB image. However, they struggle to reconstruct detailed and robust avatars simultaneously. We empirically find that the high-frequency (HF) and low-frequency (LF) information from a parametric model has the potential to enhance geometry details and improve robustness to noise, respectively. Based on this, we propose HiLo, namely clothed human reconstruction with high- and low-frequency information, which contains two components. 1) To recover detailed geometry using HF information, we propose a progressive HF Signed Distance Function to enhance the detailed 3D geometry of a clothed human. We analyze that our progressive learning manner alleviates large gradients that hinder model convergence. 2) To achieve robust reconstruction against inaccurate estimation of the parametric model by using LF information, we propose a spatial interaction implicit function. This function effectively exploits the complementary spatial information from a low-resolution voxel grid of the parametric model. Experimental results demonstrate that HiLo outperforms the state-of-the-art methods by 10.43% and 9.54% in terms of Chamfer distance on the Thuman2.0 and CAPE datasets, respectively. Additionally, HiLo demonstrates robustness to noise from the parametric model, challenging poses, and various clothing styles.(1)

关键词： 3D reconstruction Digital Human SMPL-X

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A Generative Network with Dual-Domain Discriminators for Low-Dose Stationary Sources CT Imaging 6

A Generative Network with Dual-Domain Discriminators for Low...

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6th International conference on Digital Medicine and image Processing, DMIP 2023

作者： Bai, Xiao Cheng, Ying Chen, Linjie Yang, Shuo Wang, Huamin Wang, Zhe Wu, Jiayi Cao, Guohua School of Biomedical Engineering ShanghaiTech University Shanghai China

ISBN: (纸本)9798400709425

Recent development of clinical Computed tomography (CT) technologies has led to research for novel CT systems that allow safer and faster imaging, such as low-dose cardiac CT imaging via stationary CT. However, the complex data acquisition schemes in stationary CT often cause severe artifacts and noise in the resulted images;this calls for the development of a new kind of image reconstruction algorithms. Recent advancements in deep learning have shown remarkable progress in medical image reconstruction, processing, and analysis. In this paper, we propose a generative network with dual-domain discriminators for low-dose CT reconstruction in a stationary CT system. The image-domain discriminator optimizes the generation network by comparing the generated CT images with the reference images, while the sinogram-domain discriminator preserves the structure of the sinograms and suppresses the noise. The network incorporates uncertainty to automatically adjust the weights of a multi-term loss function, eliminating the need for the manual tuning of hyperparameters in the loss function. The results from our numerical experiments demonstrate the effectiveness of our proposed reconstruction algorithm for low-dose imaging in stationary CT. © 2023 ACM.

关键词： image reconstruction

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