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11th International conference on image and Graphics (ICIG)

作者： Zhu, JingYuan Ma, HuiMin Hu, TianYu Yuan, Jian Tsinghua Univ Beijing 100084 Peoples R China Univ Sci & Technol Beijing Beijing 100083 Peoples R China

ISBN: (纸本)9783030873615;9783030873608

In this paper, a novel method is proposed for data-driven high frequency components generation of velocity fields in fluid simulation. It targets on fluid simulation based on N-S Equation which may suffer from details missing because of low simulation grid resolution and other reasons causing energy dissipation. A frequency transfer model structured with deep learning methods is designed to generate high frequency components with low frequency components as inputs. Considering that high frequency components cannot be inferred from lower ones exactly, our model provides another freedom to adjust the ratio of low frequency and high frequency components in velocity fields to obtain results with diversity and reality. A series of evaluations and results are presented to show our model's effectiveness in generating high frequency components from low frequency components without improving grid simulation to alleviating energy and dissipation and reconstruct details in fluid simulation.

关键词： Frequency transfer model Frequency components Details reconstruction Fluid simulation

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GAN-Avatar: Controllable Personalized GAN-based Human Head Avatar

GAN-Avatar: Controllable Personalized GAN-based Human Head A...

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International conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Berna Kabadayi Wojciech Zielonka Bharat Lal Bhatnagar Gerard Pons-Moll Justus Thies Max Planck Institute for Intelligent Systems Tübingen Germany University of Tübingen Max Planck Institute for Informatics Germany Tübingen AI Center 5Max Planck Institute for Informatics Germany Technical University of Darmstadt

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

Digital humans and, especially, 3D facial avatars have raised a lot of attention in the past years, as they are the backbone of several applications like immersive telepresence in AR or VR. Despite the progress, facial avatars reconstructed from commodity hardware are incomplete and miss out on parts of the side and back of the head, severely limiting the usability of the avatar. This limitation in prior work stems from their requirement of face tracking, which fails for profile and back views. To address this issue, we propose to learn person-specific animatable avatars from images without assuming to have access to precise facial expression tracking. At the core of our method, we leverage a 3D-aware generative model that is trained to reproduce the distribution of facial expressions from the training data. To train this appearance model, we only assume to have a collection of 2D images with the corresponding camera parameters. For controlling the model, we learn a mapping from 3DMM facial expression parameters to the latent space of the generative model. This mapping can be learned by sampling the latent space of the appearance model and reconstructing the facial parameters from a normalized frontal view, where facial expression estimation performs well. With this scheme, we decouple 3D appearance reconstruction and animation control to achieve high fidelity in image synthesis. In a series of experiments, we compare our proposed technique to state-of-the-art monocular methods and show superior quality while not requiring expression tracking of the training data.

关键词： Head Three-dimensional displays Telepresence Avatars Training data Aerospace electronics Hardware

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Deep learning -based sinogram extension method for interior computed tomography

Deep learning -based sinogram extension method for interior ...

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Medical Imaging conference - Physics of Medical Imaging

作者： Ketola, Juuso H. J. Heino, Helina Juntunen, Mikael A. K. Nieminen, Miika T. Inkinen, Satu I. Univ Oulu Res Unit Med Imaging Phys & Technol Oulu Finland Mikkeli Cent Hosp South Savo Hlth Care Author Oulu Finland Oulu Univ Hosp Dept Diagnost Radiol Oulu Finland Oulu Univ Hosp Med Res Ctr Oulu Oulu Finland Univ Oulu Oulu Finland

ISBN: (纸本)9781510640207

X-ray computed tomography (CT) is widely used in diagnostic imaging. Due to the growing number of CT scans worldwide, the consequent increase in populational dose is of concern. Therefore, strategies for dose reduction are investigated. One strategy is to perform interior computed tomography (iCT), where X-ray attenuation data are collected only from an internal region-of-interest. The resulting incomplete measurement is called a truncated sinogram (TS). Missing data from the surrounding structures results in reconstruction artifacts with traditional methods. In this work, a deep learning framework for iCT is presented. TS is extended with a U-net convolutional neural network, and the extended sinogram is reconstructed with filtered backprojection (FBP). U-net was trained for 300 epochs with Ll loss. Truncated and full sinograms were simulated from CT angiography slice images for training data.1097/193/152 sinograms from 500 patients were used in the training, validation, and test sets, respectively. Our method was compared with FBP applied to TS (TS-FBP), adaptive sinogram de-truncation followed by FBP (ADT-FBP), total variation regularization applied to TS, and FBPConvNet using TS-FBP as input. The best root-mean-square error (0.04 +/- 0.01, mean +/- SD) and peak signal-tonoise-ratio (29.5 +/- 2.9) dB in the test set were observed with the proposed method. However, slightly higher structural similarity indices were observed with FBPConvNet (0.97 +/- 0.01) and ADT-FBP (0.97 +/- 0.01) than with our method (0.96 +/- 0.01). This work suggests that extension of truncated sinogram data with U-Net is a feasible way to reconstruct iCT data without artifacts that render image quality undesirable for medical diagnostics.

关键词： computed tomography convolutional neural networks deep learning interior tomography image reconstruction

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Missing image data reconstruction Based on Least-Squares Approach with Randomized SVD

Missing Image Data Reconstruction Based on Least-Squares App...

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3rd International conference on Intelligent Computing and Optimization, ICO 2020

作者： Intawichai, Siriwan Chaturantabut, Saifon Department of Mathematics and Statistics Faculty of Science and Technology Thammasat University Pathumthani12120 Thailand

ISBN: (纸本)9783030681531

In this paper, we introduce an efficient algorithm for reconstructing incomplete images based on optimal least-squares (LS) approximation. Generally, LS method requires a low-rank basis set that can represent the overall characteristic of an image, which can be obtained optimally via the singular value decomposition (SVD). This basis is called proper orthogonal decomposition (POD) basis. To significantly decrease the computational cost of SVD, this work employs a randomized singular value decomposition (rSVD) to compute the basis from the available image pixels. In this work, to preserve the 2-dimensional structure of the image, the test image is first subdivided into many 2-dimensional small patches. The complete patches are used to compute the POD basis for reconstructing corrupted patches. For each incomplete patch, the known pixels in the neighborhood around the missing components are used in the LS approximation together with the POD basis in the reconstruction process. The numerical tests compare the execution time used in computing this optimal low-rank basis by using rSVD and SVD, as well as demonstrate the accuracy of the resulting image reconstructions. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Singular value decomposition

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A Regularized Limited-Angle CT reconstruction Model Based on Sparse Multi-level Information Groups of the images 16th

A Regularized Limited-Angle CT Reconstruction Model Based on...

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16th Chinese conference on image and Graphics Technologies and Applications (IGTA)

作者： Zhang, Lingli Liang, Huichuan Hu, Xiao Xu, Yi Chongqing Univ Arts & Sci Chongqing Key Lab Grp & Graph Theories & Applicat Chongqing Peoples R China Chongqing Univ Arts & Sci Chongqing Key Lab Complex Data Anal & Artificial Chongqing Peoples R China Chongqing Normal Univ Key Lab OCME Chongqing Peoples R China Chongqing Univ Engn Res Ctr Ind Computed Tomog Nondestruct Testi Educ Minist China Chongqing Peoples R China

ISBN: (纸本)9789811671890;9789811671883

Restricted by the scanning environment and the shape of the target to be detected, the obtained projection data from computed tomography (CT) are usually incomplete, which leads to a seriously ill-posed problem, such as limited-angle CT reconstruction. In this situation, the classical filtered back-projection (FBP) algorithm loses efficacy especially when the scanning angle is seriously limited. By comparison, the simultaneous algebraic reconstruction technique (SART) can deal with the noise better than FBP, but it is also interfered by the limited-angle artifacts. At the same time, the total variation (TV) algorithm has the ability to address the limited-angle artifacts, since it takes into account a priori information about the target to be reconstructed, which alleviates the ill-posedness of the problem. Nonetheless, the current algorithms exist limitations when dealing with the limited-angle CT reconstruction problem. This paper analyses the distribution of the limited-angle artifacts, and it emerges globally. Then, motivated by TV algorithm, tight frame wavelet decomposition and group sparsity, this paper presents a regularization model based on sparse multi-level information groups of the images to address the limited-angle CT reconstruction, and the corresponding algorithm called modified proximal alternating linearized minimization (MPALM) is presented to deal with the proposed model. Numerical implementations demonstrate the effectiveness of the presented algorithms compared with the above classical algorithms.

关键词： Limited-angle CT Group sparsity Wavelet tight frame Regularization Proximal alternating linearized minimization

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Unsupervised learning from incomplete measurements for inverse problems 22

Unsupervised learning from incomplete measurements for inver...

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Proceedings of the 36th International conference on Neural Information Processing Systems

作者： Julián Tachella Dongdong Chen Mike Davies Laboratoire de Physique CNRS & ENSL Lyon France School of Engineering University of Edinburgh Edinburgh UK

ISBN: (纸本)9781713871088

In many real-world inverse problems, only incomplete measurement data are available for training which can pose a problem for learning a reconstruction function. Indeed, unsupervised learning using a fixed incomplete measurement process is impossible in general, as there is no information in the nullspace of the measurement operator. This limitation can be overcome by using measurements from multiple operators. While this idea has been successfully applied in various applications, a precise characterization of the conditions for learning is still lacking. In this paper, we fill this gap by presenting necessary and sufficient conditions for learning the underlying signal model needed for reconstruction which indicate the interplay between the number of distinct measurement operators, the number of measurements per operator, the dimension of the model and the dimension of the signals. Furthermore, we propose a novel and conceptually simple unsupervised learning loss which only requires access to incomplete measurement data and achieves a performance on par with supervised learning when the sufficient condition is verified. We validate our theoretical bounds and demonstrate the advantages of the proposed unsupervised loss compared to previous methods via a series of experiments on various imaging inverse problems, such as accelerated magnetic resonance imaging, compressed sensing and image inpainting.

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Deep Learning based image Enhancing Environment with Noise Suppression

Deep Learning based Image Enhancing Environment with Noise S...

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Electronics and Renewable Systems (ICEARS), International conference on

作者： Sivanantham Basireddy Satish Kumar Reddy J. SaiGnaneswar Kalahasti Balaji K. Siva Vivek Reddy Kusam Lokesh Reddy Computer Science and Systems Engineering SVEC Tirupati Andhra Pradesh

A deep learning approach will be used to recover ancient pictures that have suffered significant damage. Unlike typical reconstruction processes that are easily handled by supervised learning methods, real-world picture degradation seems to be complex, and the system is unable to generalize due to domain differences between synthetic pictures and actual old pictures. Therefore, using huge amounts of synthetic image pairs combined with real photos, Therefore, using huge amounts of synthetic picture pairs combined with real photos, A unique triplet domain translation network. Two variational autoencoders (VAEs) have been trained to create latent spaces from both fresh and old images, respectively. The translation between two regions is thenmanaged to learn using artificially paired data. This translation normalizes well to actual photographs as the domain gap is filled in the compact latent space. The translation between these two various latent regions has been taught using artificially paired data. This translation normalizes well to images found in the real world because the compact latent space is filled with the domain gap. A global division with an incomplete nonlocal block will target structural issues like cuts and bruises and a local division attacking unstructured defects like unwanted noise and poor contrast to handle the various degradations mixed throughout an old photograph. The latent space fusion of two branches increases the ability to correct numerous flaws in old images. Convolutional neural networks (CNNs) outperform multiple-layer sequenced neural network models at identifying distinct marks, forms, and patterns in images, making them the most efficient method for processing data. The filters are applied by CNN to every pixel in the image. When it comes to visual quality, the suggested method for repairing old photographs performs better than cutting-edge techniques.

关键词： Degradation Deep learning Visualization Renewable energy sources Supervised learning Noise reduction Neural networks

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Cooperative Training and Latent Space data Augmentation for Robust Medical image Segmentation 24th

Cooperative Training and Latent Space Data Augmentation for ...

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

作者： Chen, Chen Hammernik, Kerstin Ouyang, Cheng Qin, Chen Bai, Wenjia Rueckert, Daniel Imperial Coll London Dept Comp BioMedIA Grp London England Tech Univ Munich Klinikum Rechts Isar Munich Germany Univ Edinburgh Inst Digital Commun Edinburgh Midlothian Scotland Imperial Coll London Data Sci Inst London England Imperial Coll London Dept Brain Sci London England

ISBN: (纸本)9783030871994;9783030871987

Deep learning-based segmentation methods are vulnerable to unforeseen data distribution shifts during deployment, e.g. change of image appearances or contrasts caused by different scanners, unexpected imaging artifacts etc. In this paper, we present a cooperative framework for training image segmentation models and a latent space augmentation method for generating hard examples. Both contributions improve model generalization and robustness with limited data. The cooperative training framework consists of a fast-thinking network (FTN) and a slow-thinking network (STN). The FTN learns decoupled image features and shape features for image reconstruction and segmentation tasks. The STN learns shape priors for segmentation correction and refinement. The two networks are trained in a cooperative manner. The latent space augmentation generates challenging examples for training by masking the decoupled latent space in both channel-wise and spatial-wise manners. We performed extensive experiments on public cardiac imaging datasets. Using only 10 subjects from a single site for training, we demonstrated improved cross-site segmentation performance, and increased robustness against various unforeseen imaging artifacts compared to strong baseline methods. Particularly, cooperative training with latent space data augmentation yields 15% improvement in terms of average Dice score when compared to a standard training method.

关键词： image segmentation

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Self-Guided and MR-Guided Deep-Learned Post-reconstruction PET Processing

Self-Guided and MR-Guided Deep-Learned Post-Reconstruction P...

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2021 IEEE Nuclear Science Symposium and Medical Imaging conference, NSS/MIC 2021

作者： Corda-D'incan, Guillaume Schnabel, Julia A. Reader, Andrew J. King's College London School of Biomedical Engineering and Imaging Sciences United Kingdom The Technical University of Munich Germany

ISBN: (纸本)9781665421133

Reconstructed PET images exhibit high noise levels and low spatial resolution when shorter scan times and reduced injected doses are used. Regularisation methods such as post-reconstruction smoothing can help to improve image quality. Recently, neural networks have proved to be highly effective for this task by learning an ensemble of kernels. For post-processing of PET images, a high-resolution MR image can also be used for guidance to further improve the final image quality. In this work, we investigate the impact of the input choice and the number of training samples used on a neural network's performance for PET post-reconstruction processing. To do so, six combinations of low-count PET and MR independent reconstruction outputs are fed into a state-of-the-art residual convolutional neural network (CNN). Six different networks were trained using as input i) the last iteration of a conventional PET reconstruction, ii) all the iterates from the PET reconstruction, iii) only the final PET and MR estimates, iv) all the PET estimates and the final MR, v) the final PET and all the MR estimates and vi) all the iteration outputs of independent PET and MR reconstruction. The networks have been trained using a different number of training samples as well. The results obtained suggest that using all the intermediate reconstructions lead the network to perform better when the training set size is limited. Furthermore, the gain in performance observed when the dataset size increases are higher for methods using all the intermediate reconstruction outputs. Future work will focus on training networks with a higher number of training samples to confirm the trend observed and assess the proposed method on 3D real data. © 2021 IEEE.

关键词： image quality

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Super-resolution generative adversarial networks of randomly-seeded fields

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NATURE MACHINE INTELLIGENCE 2022年第12期4卷 1165-+页

作者： Guemes, Alejandro Vila, Carlos Sanmiguel Discetti, Stefano Univ Carlos III Madrid Aerosp Engn Res Grp Leganes Spain Spanish Natl Inst Aerosp Technol INTA Subdirectorate Gen Terr Syst San Martin De La Vega Spain

reconstruction of field quantities from sparse measurements is a problem arising in a broad spectrum of applications. This task is particularly challenging when the mapping between sparse measurements and field quantities is performed in an unsupervised manner. Further complexity is added for moving sensors and/or random on-off status. Under such conditions, the most straightforward solution is to interpolate the scattered data onto a regular grid. However, the spatial resolution achieved with this approach is ultimately limited by the mean spacing between the sparse measurements. In this work, we propose a super-resolution generative adversarial network framework to estimate field quantities from random sparse sensors. The algorithm exploits random sampling to provide incomplete views of the high-resolution underlying distributions. It is hereby referred to as the randomly seeded super-resolution generative adversarial network (RaSeedGAN). The proposed technique is tested on synthetic databases of fluid flow simulations, ocean surface temperature distribution measurements and particle-image velocimetry data of a zero-pressure-gradient turbulent boundary layer. The results show excellent performance even in cases with high sparsity or noise level. This generative adversarial network algorithm provides full-field high-resolution estimation from randomly seeded fields with no need of full-field high-resolution representations for training.

关键词： Boundary layers

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