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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741-750 订阅

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MRI reconstruction by Completing Under-sampled K-space data with Learnable Fourier Interpolation 25th

MRI Reconstruction by Completing Under-sampled K-space Data ...

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25th International conference on Medical image Computing and Computer Assisted Intervention (MICCAI)

作者： Ding, Qiaoqiao Zhang, Xiaoqun Shanghai Jiao Tong Univ Inst Nat Sci Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Math Sci Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ MOE LSC Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ SJTU GenSci Joint Lab Shanghai 200240 Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Magnetic resonance imaging (MRI) acceleration is usually achieved by data undersampling, while reconstruction from undersampled data is a challenging ill-posed problem for data-missing and noisy measurements introduce various artifacts. In recent years, deep learning methods have been extensively studied for MRI reconstruction, and most of work treat the reconstruction problem as a denoising problem or replace the regularization subproblem with a deep neural network (DNN) in an optimization unrolling scheme. In this work, we proposed to directly complete the missing and corrupted k-space data by a specially designed interpolation deep neural networks combined with some convolution layers in both frequency and spatial domains. Specifically, for every missing and corrupted frequency, we use a K- nearest neighbors estimation with learnable weights. Then, two convolution neural networks (CNNs) are applied to regularize the data in both k-space and image space. The proposed DNN structures have clear interpretability for solving this undersampling problem. Extensive experiments on MRI reconstruction with diverse sampling patterns and ratios, under noiseless and noise settings demonstrate the accuracy of the proposed method compared to other learning based algorithms, while being computationally more efficient for both training and reconstruction processes.

关键词： MRI reconstruction Deep learning Compressed sensing Learnable interpolation

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Depth-based defocus elimination for macroscopic Fourier ptychography imaging 8

Depth-based defocus elimination for macroscopic Fourier ptyc...

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8th Asia Pacific conference on Optics Manufacture, APCOM 2023 and 3rd International Forum of Young Scientists on Advanced Optical Manufacturing, YSAOM 2023

作者： An, Jiating Kong, Lingbao Shanghai Engineering Research Center of Ultra-precision Optical Manufacturing School of Information Science and Technology Fudan University Shanghai China

ISBN: (纸本)9781510672673

To achieve high-resolution intensity images, Fourier ptychography (FP) has been used in microscopic for decades, and is suitable for macroscopic area. Aberration is important for the quality of reconstruction during aperture scanning and phase recovery in the FP process. In the field of macroscopic imaging, due to the defocus in the 3D scene, cameras can only capture blurry images by direct photography. To overcome the limitation, we design an FP iteration method with the depth of defocus to improve the recovery for the high-resolution images. The initial scene comprises two layers of data: The intensity information and the depth map. The spectral distribution and optical transfer function are acquired via Fourier transform. This is followed by an iterative process of reconstruction: A low-resolution image is taken at each aperture, combined with depth-induced defocus aberrations, and then substituted into optical transfer function calculations to simulate intensity measurements from camera sensing. Each scan corresponds to a specific area with limited bandwidth in Fourier space, and multiple iterations are used for phase recovery to reconstruct a high-resolution image. Compared to the traditional method, the defocus elimination FP algorithm for macroscopic imaging enhances the resolution and contrast of the reconstruction, enabling a wider range of measurable scenarios. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image reconstruction

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A fast bundle adjustment method based on track selection 4

A fast bundle adjustment method based on track selection

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4th International conference on Computer Vision and Pattern Analysis, ICCPA 2024

作者： Liu, Changwei Xiong, Pingfan Yang, Luman Chen, Song Hu, Jinquan School of Remote Sensing and Information Engineering Wuhan University Hubei Wuhan430072 China School of Geodesy and Geomatics Wuhan University Hubei Wuhan430072 China Guangdon Shenzhen518000 China Wuhan Tianjihang Information Technology Co. Ltd. Hubei Wuhan430074 China

ISBN: (纸本)9781510682528

Bundle adjustment is the core of the Structure from Motion algorithm, and it is also a very time-consuming part, in which redundant observations and initial parameter values with large errors increase the time consumption of the algorithm. In order to improve the efficiency of bundle adjustment, we propose an track selection method based on uniformity, accuracy, coverage and connectivity criteria. Firstly, we divide the space of tracks into several 3D grids. Secondly, we start from the grid with the largest number of tracks, and eliminate the redundant tracks with low connectivity and low accuracy in each grid, while ensuring the connectivity and coverage of tracks. Finally, we use a variety of experimental data to verify this algorithm. The result shows that the algorithm can delete a large number of redundant tracks, and effectively improve the efficiency of the bundle adjustment method on the premise of ensuring the accuracy. When the track retention rate is 0.4, the efficiency of the bundle adjustment method is increased by about 2 times, and the corresponding precision loss value is 0.026. © 2024 SPIE.

关键词： Tunable filters 3D tracking image restoration Point clouds 3D image reconstruction image filtering image processing Pose estimation 3D modeling Atomic force microscopy

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A Low-Cost Three-Cameras Photogrammetric System for Training Students in Physical Simulation of Shallow Landslides 26th

A Low-Cost Three-Cameras Photogrammetric System for Training...

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26th Italian conference on Geomatics for Environmental Monitoring: from data to Services (ASITA)

作者： Scaioni, Marco Ovalle, Karen Lorena Gonzalez Eskandari, Rasoul Perfetti, Luca Politecn Milan Dept Architecture Built Environm & Construct Engn Via Ponzio 31 I-20133 Milan Italy Politecn Milan Lecco CampusVia Previati 1-C I-23900 Lecce Italy Univ Brescia Dept Civil Environm Architectural Engn & Math Via Branze 43 I-25123 Brescia Italy

ISBN: (纸本)9783031599248;9783031599255

Simulation of shallow landslides in a scaled-down facility has become a common methodology for investigating slope stability. Multiple parameters may be replicated under different triggering factors, which mainly include water as rainfall or seepage from an artificial basin or pipeline. Landslide simulators are important tools for scientific investigation and to test technical solutions for monitoring and mitigation, but they may also be used for training of students at different education levels. A Landslide Simulator (LS) has been established in 2016 on the Lecco Campus of Politecnico Milano, Italy, which is equipped with three different groups of geotechnical, geophysical, and imaging/ranging sensors. This paper is focused on a low-cost Three-Cameras Photogrammetric System (3CPS) recently implemented, based on industrial FLIR cameras mounted on a bar to be placed over the slope in the LS. This is applied during landslide simulation experiments for two purposes: (1) 2D Digital image Correlation (2D-DIC);and (2) 3D Photogrammetric reconstruction. Two sets of coded targets are located on both sides of the flume to serve as ground control points. data processing is carried out by students based on two software packages provided by the university: one popular Matlab (R) code for 2D-DIC, and Agisoft Metashape Professional (R). A specific validation study has been developed and illustrated in the paper, together with its application to a couple of landslide simulation experiments.

关键词： Digital image Correlation Landslides Photogrammetry Simulation Training

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On Moving Object Segmentation from Monocular Video with Transformers

On Moving Object Segmentation from Monocular Video with Tran...

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IEEE/CVF International conference on Computer Vision (ICCV)

作者： Homeyer, Christian Schnoerr, Christoph Robert Bosch GmbH Corp Res Comp Vis Lab Hildesheim Heidelberg Germany Heidelberg Univ Image & Pattern Anal Grp Heidelberg Germany

ISBN: (纸本)9798350307443

Moving object detection and segmentation from a single moving camera is a challenging task, requiring an understanding of recognition, motion and 3D geometry. Combining both recognition and reconstruction boils down to a fusion problem, where appearance and motion features need to be combined for classification and segmentation. In this paper, we present a novel fusion architecture for monocular motion segmentation - M3Former, which leverages the strong performance of transformers for segmentation and multi-modal fusion. As reconstructing motion from monocular video is ill-posed, we systematically analyze different 2D and 3D motion representations for this problem and their importance for segmentation performance. Finally, we analyze the effect of training data and show that diverse datasets are required to achieve SotA performance on Kitti and Davis. Code will be released upon publication.

关键词： 3D dynamic video monocular motion estimation multi modal fusion segmentation transformer

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Depth estimation in turbid media from stack of epi-illuminated microscopy images, using deep learning 3

Depth estimation in turbid media from stack of epi-illuminat...

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conference on Tissue Optics and Photonics III

作者： Ghosh, Anindya Hohmann, Martin Klampfl, Florian Schmidt, Michael Friedrich Alexander Univ Erlangen Nurnberg Inst Photon Technol LPT Konrad Zuse Str 3-5 D-91052 Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Erlangen Grad Sch Adv Opt Technol SAOT Paul Gordan Str 6 D-91052 Erlangen Germany

ISBN: (纸本)9781510673380;9781510673397

Optical imaging is the simplest modality for any imaging applications. Also, microscopy has the best resolution among all the imaging modalities used for clinical diagnosis and biomedical research. However, detection of inclusions by optical imaging in a biological tissue might be challenging because of the scattering and absorption inside the tissue. Due to the scattering, an optical image of an object inside a turbid medium yields a scattering-induced blur, which increases with the depth of the object from the surface of the medium. This correlation has been shown in literature to be used for depth estimation from single trans-illumination images. In a similar way, the scattering-blur also changes while varying the focal plane of the imaging system. This gradual change in the contrast can be utilised to determine the actual position of the object from a stack of multiple focus-shifted images. Here, we present a proof of concept of a deep-learning based method for determining the location of objects inside turbid media, from a stack of blurred images. Since trans-illumination is not applicable for large body-parts, epi-illumination setup was used for imaging. For this preliminary study, a U-Net neural network regression model was trained and tested under simplified conditions. It takes 3D image data as input and gives a 2D depth matrix as output. Black PU structures of simple geometrical shapes were used as absorbing objects. An intra-lipid solution of 0.7% concentration was used as the scattering substrate. The black absorbers, immersed in the substrate, were imaged with microscope by varying the focal plane to obtain the image-stacks. The actual depth profile of the absorbers was measured with a 3D profilometer, which was used as corresponding ground truth for training. The predicted results from the trained model show good agreement with the ground truth for testing data.

关键词： Depth estimation depth reconstruction turbid media scattering-blur image-stack deep learning U-net

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Frequency Domain Filtering Based Compressed Sensing Applied on Sparse-Angle CT image reconstruction 2022

Frequency Domain Filtering Based Compressed Sensing Applied ...

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7th International conference on Multimedia and image Processing, ICMIP 2022

作者： Dong, Jian Chen, Hao Yang, Xiaoxia Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin300222 China Department of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9781450387408

In the process of CT scanning, multi-Angle projection data needs to be obtained from a large number of projection actions, which makes the scanned individual bear the risk of high radiation exposure. In order to solve such problems, the use of sparse projection data for CT image reconstruction is proposed as a new type of solution. The previous research can obtain good quality reconstructed images when the projection data is sparse by using the CT reconstruction technology based on the nonlinear sparsity transformation of compressed sensing. However, the heavy time loading of the image reconstruction is a practical problem that needs to be solved urgently. This study optimizes the non-linear filtering process of the regularization term of the original scheme, and proposes a novel method which replaces the original non-linear filter with a low-pass frequency domain filter. This strategy effectively utilizes the properties of low-pass frequency domain filtering in image processing. The excellent properties include high efficiency and low time complexity for image smoothing. The simulation experiment results show that in the process of CT image reconstruction using compressed sensing algorithm, the low-pass frequency domain filtering of the new scheme can greatly reduce the required time in the reconstruction of sparse projection data, and the image quality is feasibly guaranteed. © 2022 ACM.

关键词： Compressed sensing

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Small Objects Detection for incomplete Annotated Cervical Cancer Cells dataset 6

Small Objects Detection for Incomplete Annotated Cervical Ca...

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6th IEEE International conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Huang, Qiubo Chen, Xing Gong, Runze Zhang, Yakui Chen, Dehua Wang, Mei Donghua University School of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350325485

Traditional object detectors usually require fully annotated instances for training to achieve satisfactory detection results. However, for incompletely annotated image instances, especially in medical imaging, their detection results are still far from satisfactory due to the difficulty for sample annotation. In this regard, Co-mining self-supervised learning for incompletely annotated object detection performs excellently. However, its effectiveness in detecting smaller objects is not yet satisfactory. Therefore, this paper proposes an improved model called CO-FCOS to design and implement a better incompletely annotated detection algorithm for small objects. In order to fully utilize the position and category information of existing objects in the image, more effective data augmentation strategies are applied, and the predictions are generated by detectors in two branches respectively. To further improve the model's adaptability and generalization performance under different scenarios and data distributions, an OTA dynamic label assignment strategy is introduced. In addition, the label consistency strategy breaks the limitations of the original model, generates more pseudo labels for the model, which enriches the training data, and improves the model performance. Experimental results show that, compared to the Co-mining model, our proposed CO-FCOS model improves the accuracy by 4.3 mAP on the cervical cancer TCT dataset and by 1.7 mAP on the COCO-miss50 incomplete annotation dataset. © 2023 IEEE.

关键词： Object detection

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Poisson Noise image Restoration Based on Bregman Proximal Gradient 6

Poisson Noise Image Restoration Based on Bregman Proximal Gr...

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6th International conference on Computer Network, Electronic and Automation, ICCNEA 2023

作者： Li, Huan Zhang, Wenjuan Huang, Shujian Xiao, Feng Xi'an Technological University School of Sciences Shaanxi China Xi'an Technological University School of Computer Science and Engineering Shaanxi China

ISBN: (纸本)9798350305388

In view of the fact that data item functions are not smooth in Poisson noise image restoration, while most existing first-order optimization methods require data item smoothness, a Poisson noise image restoration method based on BPG (Bregman Proximal Gradient) algorithm was proposed. Firstly, we establish a Poisson noise image recovery model using the maximum posterior estimation framework. This model aims to recover a high-quality image from noisy input data corrupted by Poisson noise. The model's data term is determined by the Kullback-Leibler (KL) divergence, which corresponds to the likelihood estimation. The KL divergence measures the difference between the probability distributions of the noisy input data and the recovered image. The regular term uses the Tikhonov regular term, which corresponds to a priori estimation. Secondly, the BPG algorithm is employed to address the model, which gives a wider and more satisfactory smoothness based on Bregman distance to replace the traditional gradient Lipschitz smoothness requirements, and maintains the convergence speed of PG (Proximal Gradient) algorithm. Finally, numerical experimental results, it proposed method outperforms the conventional algorithm by a margin of 0.01 - 6.94 (db) in terms of recovering Poisson noise images, and it also outperforms several algorithms in visual effects. © 2023 IEEE.

关键词： image reconstruction

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Shadow Removal from a Single image Using an Enhanced Generative Adversarial Network 19

Shadow Removal from a Single Image Using an Enhanced Generat...

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19th International conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Zhang, Jing Kong, Weiwei Xi'an University of Posts and Telecommunications College of Computer Science Xi'an China Xi'an University of Posts and Telecommunications The Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China

ISBN: (纸本)9798350304398

image shadow removal is challenging due to the diversity of shadows and the dependence on the background of the area covered by shadows. In this paper, we design an enhanced generative adversarial network (EGAN) to handle the shadow removal task. EGAN is a complicated generative adversarial network that may exploit previous info, like shadow masks and lightness estimation, for improving recovery capability. In addition, EGAN can effectively utilize the details of shadow regions and eliminate artifacts. EGAN owns three main advantages. First, EGAN employs stricter rules for discriminators, which can upgrade the instability of traditional GAN. Second, EGAN involves a completely unique enhanced attention module, which may efficiently pick up different types of previous info to accelerate the learning efficiency of image reconstruction. Finally, EGAN is essentially an original cascaded network, that is, DRDU, which is beneficial for efficiently detecting hierarchical features of image recovery. Simulation experiments are conducted to verify the effectiveness of the proposed method on two benchmark datasets including SRD and ISTD, the strengths of our proposed method are indicated over the current representative ones according to subjective performance and objective estimation indexes. © 2023 IEEE.

关键词： image reconstruction

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