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

共 14811 条记录，以下是641-650 订阅

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A High-Resolution Imaging Algorithm For Synthetic Aperture Interferometric Radiometry Based on Unsupervised Pulse-Coupled Neural Networks

A High-Resolution Imaging Algorithm For Synthetic Aperture I...

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IET International Radar conference 2023, IRC 2023

作者： Chen, Xinpeng Zhu, Dong Hu, Fei School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781839539954

High-resolution imaging of synthetic aperture interferometric radiometry (SAIR) has been a research hotspot. Since the spatial resolution of SAIR is limited by array aperture size and the inversion process is a pathological problem, achieving high-resolution image is challenging. In most cases, the target's brightness temperature in SAIR contaminated by noise differs from the brightness temperature of its surrounding pixels, and the noise destroys the correlation between the brightness temperature of adjacent pixels, resulting in low-resolution SAIR *** address this issue, this paper proposes a high-resolution SAIR imaging algorithm based on unsupervised Pulse-Coupled Neural Network (PCNN) learning. This algorithm adjusts the corresponding brightness temperature of each pixel based on the firing status of the neuron and its neighboring neurons, reducing noise and achieving high-resolution reconstruction of SAIR images. The algorithm does not require a significant amount of training data or prior information, and can achieve the high-resolution reconstruction by using a single image. Simulation and experimental results demonstrate that the proposed algorithm can improve the peak signal-to-noise ratio and resolution of targets. © The Institution of Engineering & Technology 2023.

关键词： image reconstruction

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Application of graph networks to γ/hadron separation in IACT image analyses 38

Application of graph networks to γ/hadron separation in IAC...

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38th International Cosmic Ray conference, ICRC 2023

作者： Glombitza, Jonas Joshi, Vikas Bruno, Benedetta Funk, Stefan Erlangen Centre for Astroparticle Physics Nikolaus-Fiebiger-Strasse 2 Erlangen Germany

Imaging Atmospheric Cherenkov Telescopes (IACTs) enable precise ground-based observations of the gamma-ray sky by imaging the distribution of Cherenkov light emitted during the development of air showers. Nowadays, many reconstruction algorithms rely on an elliptical high-level parameterization of these IACT images — the Hillas parameterization — and exploit their correlations. To overcome the limitations of the elliptical modeling, besides sophisticated analytical or template-based models, the advent of deep learning allows for reconstruction techniques that showed first promising results. By interpreting the detected images as a collection of triggered sensors that graphs can represent, we propose an algorithm based on graph networks for stereoscopic IACT image analyses. For images cleaned from background noise, this allows for an efficient algorithm design that bypasses the challenge of sparse images that occur in deep learning approaches based on convolutional neural networks. We investigate graph network architectures to two different stereoscopic data sets, simulated for the H.E.S.S. experiment. The algorithm enables an excellent γ/hadron separation with improvements to classical machine learning. Further, we find that the algorithm offers promising prospects for stereoscopic reconstructions also for telescopes featuring different camera geometries. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Stereo image processing

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Shot-Gather reconstruction on 3D incomplete Surveys Using Deep Prior-Based Convolutional Neural Networks 4

Shot-Gather Reconstruction on 3D Incomplete Surveys Using De...

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4th HGS/EAGE conference on Latin America

作者： Rodríguez, L.M. León-López, K. Goyes-Peñafiel, P. Galvis, L. Arguello, H. Universidad Industrial de Santander Colombia

ISBN: (纸本)9789462824324

Seismic surveys are often affected by irregularity and coarse sampling resulting from limitations at the acquisition such as obstacles and environmental constraints. Thus, various processing algorithms have been developed to reconstruct these missing data, including interpolation based on transforms and deep learning-based reconstruction methods, where the latter allows the extraction of more complex structures from tons of data. Alternatively, deep-image-prior (DIP) methods mitigate the use of extensive training information by considering only the data under observation, showing promising results in recovering seismic traces. This work proposes a DIP-based convolutional neural network for recovering missing sources in a three-dimensional (3D) seismic survey which exploits as priors the data acquired and the structure of the 3D network. Additionally, considering the correlation across the source domain, an operator is employed for vertically shifting the set of traces to have the maximum data correlation in the network. Numerical experiments show that the proposed network recovers the complete set of traces in each shot-gather, where the information and events are preserved even though the source is not acquired. © 2022 HGS/*** rights reserved.

关键词： Convolutional neural networks

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ECT image reconstruction based on Improved Sensitivity Field 6

ECT Image Reconstruction based on Improved Sensitivity Field

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6th IEEE Information Technology and Mechatronics Engineering conference, ITOEC 2022

作者： Hou, Fang Song, Menghan Liu, Shi China

ISBN: (纸本)9781665431859

In the actual industrial production process, we often encounter two or more kinds of objects mixed flow conditions, this phenomenon is generally known as multiphase flow in academia and industry. Process Tomography (PT) is an important technique for Process detection in two-phase flow. Electrical Capacitance Tomography (ECT) is a kind of process Tomography technology. It has the advantages of non-invasive, low cost, simple structure, etc. It can be applied as long as the dielectric constant of each phase is different between the media. Its principle is to use a set of sensors installed on the device under test array electrodes, formed from different angles analyte scanning space of sensitive field, according to the measured object in the different medium has different dielectric constant of the properties, and the implementation of the distribution of the field of incomplete information extraction, using the appropriate image reconstruction algorithm, each phase of analyte pipe field distribution in the form of a visual image. This paper mainly improves the quality of reconstructed images by improving the sensitive field, and jointly uses ANSYS Maxwell, C++ and MATLAB for research. © 2022 IEEE.

关键词： image reconstruction

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Low-dose CT image reconstruction based on a deep neural network 10

Low-dose CT image reconstruction based on a deep neural netw...

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10th IEEE Joint International Information Technology and Artificial Intelligence conference, ITAIC 2022

作者： Li, Jing Huang, Xiaoyan School of Electronic Engineering Xi'an Shiyou University Xi'an China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Computed tomography (CT) technology plays an important role in the field of medicine. It helps the doctor give the accurately judgement of lesions through the clear CT image. But the patients can't afford a large amount of radiation to obtain the clear CT image. Therefore, the problem of low-dose CT image reconstruction is particularly prominent. This paper presents a low-dose CT image reconstruction method based on deep learning algorithm. The multi-layers convolution network is constructed to deeply mine the features of the input data, so as to weaken the noise signal in the original data and obtain a clear and identifiable reconstructed image. This method aims at the processing of low-dose projection data, avoids the radiation damage of the irradiated party, and reduces the energy loss of the equipment. Through the analysis of the experimental results, this paper summarizes the future work in image detail information capture and database expansion, so as to improve the overall image reconstruction quality. © 2022 IEEE.

关键词： image reconstruction

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Solving the Plane-Sphere Ambiguity in Top-Down Structure-from-Motion

Solving the Plane-Sphere Ambiguity in Top-Down Structure-fro...

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

作者： Haalck, Lars Risse, Benjamin Univ Munster Inst Geoinformat Munster Germany Univ Munster Inst Comp Sci Munster Germany

ISBN: (纸本)9798350318920;9798350318937

Drone-based land surveys and tracking applications with a moving camera require three-dimensional reconstructions from videos recorded using a downward facing camera and are usually generated by Structure-from-Motion (SfM) algorithms. Unfortunately, monocular SfM pipelines can fail in the presence of lens distortion due to a critical configuration resulting in a plane-sphere ambiguity which is characterized by severe curvatures of the reconstructions and erroneous relative camera pose estimations. We propose a 4-point minimal solver for the relative pose estimation for two views sharing the same radial distortion parameters (i.e. from the same camera) with a viewing direction perpendicular to the ground plane. To extract 3D reconstructions from continuous videos, the relative pose of pairwise frames is estimated by using the solver with RANSAC and the Sampson error where globally consistent distortion parameters are determined by taking the medial of all values. Moreover, we propose an additional regularizer for the final bundle adjustment to remove any remaining curvature of the reconstruction if necessary. We tested our methods on synthetic and real-world data and our results demonstrate a significant reduction of curvature and more accurate relative pose estimations. Our algorithm can be easily integrated into existing pipelines and is therefore a practical solution to resolve the plane-sphere ambiguity in a variety of top-down SfM applications.

关键词： 3D computer vision Algorithms Algorithms image recognition and understanding

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image reconstruction in Phase-Contrast CT with Shortened Scans 7

Image Reconstruction in Phase-Contrast CT with Shortened Sca...

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7th International conference on image Formation in X-Ray Computed Tomography

作者： Zhang, Zheng Chen, Buxin Xia, Dan Sidky, Emil Y. Anastasio, Mark Pan, Xiaochuan Department of Radiology The University of Chicago ChicagoIL United States Department of Radiation & Cellular Oncology The University of Chicago ChicagoIL United States Department of Bioengineering The University of Illinois at Urbana-Champaign ChampaignIL United States

ISBN: (纸本)9781510656697

Phase-contrast CT (PCCT) is an emerging tool that has found numerous applications, including applications to preclinical imaging. There remains a need for reducing the imaging time in current PCCT. One approach to reducing imaging time is to reduce the scanning angular range in PCCT. However, accurate image reconstruction from data collected over a limited angular range (LAR) is challenging because it poses a problem of accurate inversion of the PCCT imaging model that can be highly ill-conditioned in LAR scans. In this work, we conduct an investigation of accurate image reconstruction through inverting the imaging model for LAR scanning configurations in propagation-based (PB) PCCT. We have developed a directional-total-variation (DTV) algorithm for image reconstruction from knowledge of the discrete X-ray transform (DXT) over a LAR for CT imaging. Observing the mathematical similarity between the DXT in CT and the imaging model in PB-PCCT, we develop and tailor the DTV algorithm for image reconstruction from LAR data in PB-PCCT. Results of our study show that the tailored DTV algorithm can yield image reconstruction with reduced LAR artifacts that can be observed otherwise in images reconstructed by use of the existing algorithm in PB-PCCT imaging. For a given LAR, it can be divided into sub arcs of LARs. We also investigate a scanning configuration with two orthogonal arcs of LARs separated by 90◦, and observe that the two-orthogonal-arc scanning configuration may allow image reconstruction more accurately than does a single-arc scanning configuration even though the total angular ranges in both scanning configurations are identical. While boundary images can be reconstructed from data, we develop the DTV algorithm for reconstruction of the image, i.e., the refractive index distribution, instead of its boundary image from data in PB-PCCT. Once the image is obtained, the Laplacian operator can be applied to it for yielding its boundary image. © 2022 SPIE.

关键词： image reconstruction

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Research on Traditional Art image reconstruction Method Based on Computer Vision Analysis 2

Research on Traditional Art Image Reconstruction Method Base...

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2nd IEEE International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Supriya, M. Anvesh, Kuncham Muruganandham, B. Manasa Krishnan, K. Muthu, R. Murugan, K. Kpr Institute of Engineering and Technology Department of Ece India

ISBN: (纸本)9798350369960

In the framework of the digital era, the technology of image processing is one of the technologies that is being used increasingly often in all aspects of modern life. image correction may be handled using algorithms based on computer vision, which can increase the correction's influence on the image as a whole. The conventional method of identifying structural systems collects data via the use of wired or wireless measuring systems. The process of establishing sensors and the equipment used for data collection is laborious and takes a lot of time. The process has two stages: the first is the extraction of the line drawing, and the second is the rendering of the line drawing. An edge detection approach that integrates low-level and middle-level visual information is used to extract the line drawing in the process of on-line drawing extraction. This method is used to extract the drawing. The edges are then simplified and linked in accordance with the computer vision theory. Additionally, the image processing technology is used as the research object in order to develop a scientific model for the distorted image. These steps are based on the findings of traditional edge detection. image restoration can be helped along by filtering samples of the image and getting rid of any noise that is there. The fundamentals of computer vision and the technologies that are connected to it are broken down in detail, and the behavioral patterns of visual interaction are investigated. The image of Chinese traditional art serves both the role of art communication and as a medium and symbol for cultural communication. At the same time, it carries on the legacy of a fine traditional culture that has been passed down from generation to generation in China. The image processing technology that is based on computer vision algorithms provides a bigger number of benefits, in addition to a better degree of accuracy. © 2023 IEEE.

关键词： Computer Vision feedback image reconstruction Traditional art Visual analysis

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FreqFormer: Frequency-aware Transformer for Lightweight image Super-resolution 33

FreqFormer: Frequency-aware Transformer for Lightweight Imag...

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33rd International Joint conference on Artificial Intelligence (IJCAI)

作者： Dai, Tao Wang, Jianping Guo, Hang Li, Jinmin Wang, Jinbao Zhu, Zexuan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9781956792041

Transformer-based models have been widely and successfully used in various low-vision visual tasks, and have achieved remarkable performance in single image super-resolution (SR). Despite the significant progress in SR, Transformer-based SR methods (e.g., SwinIR) still suffer from the problems of heavy computation cost and low-frequency preference, while ignoring the reconstruction of rich high-frequency information, hence hindering the representational power of Transformers. To address these issues, in this paper, we propose a novel Frequency-aware Transformer (FreqFormer) for lightweight image SR. Specifically, a Frequency Division Module (FDM) is first introduced to separately handle high- and low-frequency information in a divide-and-conquer manner. Moreover, we present Frequency-aware Transformer Block (FTB) to extracting both spatial frequency attention and channel transposed attention to recover high-frequency details. Extensive experimental results on public datasets demonstrate the superiority of our FreqFormer over state-of-the-art SR methods in terms of both quantitative metrics and visual quality. Code and models are available at https://***/JPWang-CS/FreqFormer.

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A Deep Kernel Method for PET image reconstruction

A Deep Kernel Method for PET Image Reconstruction

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conference on Medical Imaging - image Processing

作者： Li, Siqi Wang, Guobao Univ Calif Davis Med Ctr Dept Radiol Sacramento CA 95817 USA

ISBN: (数字)9781510649408

ISBN: (纸本)9781510649408;9781510649392

image reconstruction for positron emission tomography (PET) is challenging because of the ill-conditioned tomographic problem and low counting statistics. Kernel methods address this challenge by using kernel representation to incorporate image prior information in the forward model of iterative PET image reconstruction. Existing kernel methods construct the kernels commonly using an empirical procedure, which may lead to suboptimal performance. In this paper, we describe the equivalence between the kernel representation and a trainable neural network model. A deep kernel method is proposed with the training process utilizing available image prior to seek the best way to form a set of robust kernels optimally rather than empirically. The results from computer simulations and a real patient dataset demonstrate that the proposed deep kernel method can outperform existing kernel method and neural network method for dynamic PET image reconstruction.

关键词： Positron emission tomography data modeling image restoration Neural networks reconstruction algorithms Expectation maximization algorithms Signal to noise ratio Feature extraction image processing Computer simulations

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