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

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A ROBUST FRAMEWORK FOR GEOTIME CUBE

A ROBUST FRAMEWORK FOR GEOTIME CUBE

引用

15th IEEE International conference on image Processing (ICIP 2008)

作者： Toujas, v. Donias, M. Jeantet, D. Guillon, S. Berthoumieu, Y. Univ Bordeaux 1 ENSEIRB ENSCPBGrp Signal Lab IMSUMR 5218 351 Cours Liberat F-33405 Talence France TOTAL CSTJF F-64018 Pau France

ISBN: (纸本)9781424417650

This paper presents a robust unsupervised framework for 3D seismic data flattening. The resulting volume, called GeoTime cube, brings to light history of sedimentary deposits which is a key issue in petroleum prospecting. The proposed method makes it possible to obtain the transformation by transcribing fundamental principles of geophysics in image processing. The first step is a sedimentary layer reconstruction, the second one consists in numbering them according to their relative geological age and the last one computes a transformation in order to clearly represent them in a flattened way. Finally, the results obtained by our method compared to an existing one show that many relevant information can be extracted from GeoTime cubes and the final flattened data enhances the seismic structures identification.

关键词： Seismic imaging GeoTime Flattening Surface reconstruction Partial Differential Equations

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SNeS: Learning Probably Symmetric Neural Surfaces from incomplete data 17th

SNeS: Learning Probably Symmetric Neural Surfaces from Incom...

引用

17th European conference on Computer vision (ECCv)

作者： Insafutdinov, Eldar Campbell, Dylan Henriques, Joao F. vedaldi, Andrea Univ Oxford Oxford OX1 3PJ England

ISBN: (纸本)9783031198236;9783031198243

We present a method for the accurate 3D reconstruction of partly-symmetric objects. We build on the strengths of recent advances in neural reconstruction and rendering such as Neural Radiance Fields (NeRF). A major shortcoming of such approaches is that they fail to reconstruct any part of the object which is not clearly visible in the training image, which is often the case for in-the-wild images and videos. When evidence is lacking, structural priors such as symmetry can be used to complete the missing information. However, exploiting such priors in neural rendering is highly non-trivial: while geometry and non-reflective materials may be symmetric, shadows and reflections from the ambient scene are not symmetric in general. To address this, we apply a soft symmetry constraint to the 3D geometry and material properties, having factored appearance into lighting, albedo colour and reflectivity. We evaluate our method on the recently introduced CO3D dataset, focusing on the car category due to the challenge of reconstructing highly-reflective materials. We show that it can reconstruct unobserved regions with high fidelity and render high-quality novel view images.

关键词： 3D reconstruction Novel view synthesis Neural rendering

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Efficient MR image reconstruction for compressed MR imaging

引用

13th International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Huang, Junzhou Zhang, Shaoting Metaxas, Dimitris Dept Comp Sci Piscataway NJ 08854 USA

ISBN: (纸本)3642157041

In this paper, we propose an efficient algorithm for MR image reconstruction. The algorithm minimizes a linear combination of three terms corresponding to a least square data fitting, total variation (Tv) and L1 norm regularization. This has been shown to be very powerful for the MR image reconstruction. First, we decompose the original problem into L1 and Tv norm regularization subproblems respectively. Then, these two subproblems are efficiently solved by existing techniques. Finally, the reconstructed image is obtained from the weighted average of solutions from two subproblems in an iterative framework. We compare the proposed algorithm with previous methods in term of the reconstruction accuracy and computation complexity. Numerous experiments demonstrate the superior performance of the proposed algorithm for compressed MR image reconstruction. (C) 2011 Elsevier B.v. All rights reserved.

关键词： Convex optimization Compressive sensing MR image reconstruction

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Multi-frame blind deconvolution with linear equality constraints

Multi-frame blind deconvolution with linear equality constra...

引用

2nd conference on image reconstruction from incomplete data

作者： Löfdahl, MG Royal Swedish Acad Sci AlbaNova Univ Ctr Inst Solar Phys SE-10691 Stockholm Sweden

ISBN: (纸本)0819445592

\The Phase Diverse Speckle (PDS) problem is formulated mathematically as Multi Frame Blind Deconvolution (MFBD) together with a set of Linear Equality Constraints (LECs) on the wavefront expansion parameters. This MFBD-LEC formulation is quite general and, in addition to PDS, it allows the same code to handle a variety of different data collection schemes specified as data, the LECs, rather than in the code. It also relieves us from having to derive new expressions for the gradient of the wavefront parameter vector for each type of data set. The idea is first presented with a simple formulation that accommodates Phase Diversity, Phase Diverse Speckle, and Shack-Hartmann wavefront sensing. Then various generalizations are discussed, that allows many other types of data sets to be handled. Background: Unless auxiliary information is used, the Blind Deconvolution problem for a single frame is not well posed because the object and PSF information in a data frame cannot be separated. There are different ways of bringing auxiliary information to bear on the problem. MFBD uses several frames which helps somewhat, because the solutions are constrained by a requirement that the object be the same, but is often not enough to get useful results without further constraints. One class of MFBD methods constrain the solutions by requiring that the PSFs correspond to wavefronts over a certain pupil geometry, expanded in a finite basis. This is an effective approach but there is still a problem of uniqueness in that different phases can give the same PSF. Phase Diversity and the more general PDS methods are special cases of this class of MFBD, where the observations are usually arranged so that in-focus data is collected together with intentionally defocused data, where information on the object is sacrificed for more information on the aberrations. The known differences and similarities between the phases are used to get better estimates.

关键词： wavefront sensing deconvolution phase diversity inverse problems image restoration Shack-Hartmann

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Analysis of the influence of multiple scattering on the reconstruction of ultrasound tomography signals using synthetic aperture focussing technique

Analysis of the influence of multiple scattering on the reco...

引用

2012 19th International conference on Systems, Signals and image Processing, IWSSIP 2012

作者： Hardt, M. Ruiter, N.v. Gemmeke, H. Germany

ISBN: (纸本)9783200023284

At KIT we are developing a 3D Ultrasound Computer Tomograph (USCT) for breast cancer detection. Our current reconstruction algorithm, Synthetic Aperture Focussing Technique (SAFT) assumes single scattering for the reconstruction of 3D images from the measured data. Higher order reflections cause artefacts in the reconstruction. © 2012 Institute of Telecommunica.

关键词： Tomography

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Magnetic resonance image reconstruction by nullspace based finite rate of innovation framework 21

Magnetic resonance image reconstruction by nullspace based f...

引用

12th Indian conference on Computer vision, Graphics and image Processing, ICvGIP 2021

作者： Reddy, P. Sudhakar Raghavendra, B.S. Narasimhadhan, A.v. National Institute of Technology Karnataka Mangalore India

ISBN: (纸本)9781450391276

The finite rate of innovation (FRI) framework has proved that it is possible to reconstruct the analog signals which have a finite number of parameters. FRI framework is used to reconstruct the images from undersampled magnetic resonance (MR) data. The reconstruction of the MR image from the MR data is a estimation problem, which can be solved by utilizing Prony's method. However, Prony's method involves solving the polynomial roots of the annihilating filter and this fact leads to an unstable reconstruction in the high noise scenario. In this paper, we introduce a novel reconstruction approach is also based on the annihilating filter. However, it involves the use of solutions of an underdetermined linear system. The simulation results of the proposed reconstruction approach show that the peak signal to noise ratio (PSNR) and the structural similarity index measure (SSIM) are higher magnitude than that of conventional FRI methods in the high noise scenario.1 © 2021 ACM.

关键词： Magnetic resonance imaging

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OPTIMAL OPENINGS FOR OvERLAPPING SIGNAL AND NOISE GRAINS 5

OPTIMAL OPENINGS FOR OVERLAPPING SIGNAL AND NOISE GRAINS

引用

conference on image Algebra and Morphological image Processing v

作者： CUCIUREANZAPAN, C DOUGHERTY, ER ROCHESTER INST TECHNOL CTR IMAGING SCIROCHESTERNY 14623

ISBN: (纸本)081941624X

Optimal openings are considered for extraction of signal from noise in the random binary union-noise model. Disjointness of signal and noise is not assumed, nor are grains within the signal or within the noise assumed to be disjoint. There is a constraint on the overlapping, but this reflects the manner in which binary granular images are derived from gray-scale images of touching objects. The method assumes that the degraded image is segmented by the binary watershed algorithm and that an optimal opening by reconstruction must be found to remove segmented noise grains while passing segmented signal grains.

关键词： Interference (communication) image segmentation Mathematical modeling Signal processing Binary data Silicon image processing Optimization (mathematics) reconstruction algorithms image processing algorithms and systems

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Signal Domain Learning Approach for Optoacoustic image reconstruction from Limited view data 5

Signal Domain Learning Approach for Optoacoustic Image Recon...

引用

5th International conference on Medical Imaging with Deep Learning (MIDL)

作者： Susmelj, Anna Klimovskaia Lafci, Berkan Ozdemir, Firat Davoudi, Neda Dean-Ben, Xose Luis Perez-Cruz, Fernando Razansky, Daniel Swiss Fed Inst Technol Swiss Data Sci Ctr Zurich Switzerland EPFL Zurich Switzerland Univ Zurich Inst Pharmacol & Toxicol Fac Med Zurich Switzerland Univ Zurich Inst Biomed Engn Fac Med Zurich Switzerland Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Inst Biomed Engn Zurich Switzerland Swiss Fed Inst Technol Dept Comp Sci Inst Machine Learning Zurich Switzerland

Multi-spectral optoacoustic tomography (MSOT) relies on optical excitation of tissues with subsequent detection of the generated ultrasound waves. Optimal image quality in MSOT is achieved by detection of signals from a broad tomographic view. However, due to physical constraints and other cost-related considerations, most imaging systems are implemented with probes having limited tomographic coverage around the imaged object, such as linear array transducers often employed for clinical ultrasound (US) imaging. MSOT image reconstruction from limited-view data results in arc-shaped image artifacts and disrupted shape of the vascular structures. Deep learning methods have previously been used to recover MSOT images from incomplete tomographic data, albeit poor performance was attained when training with data from simulations or other imaging modalities. We propose a two-step method consisting of i) style transfer for domain adaptation between simulated and experimental MSOT signals, and ii) supervised training on simulated data to recover missing tomographic signals in realistic clinical data. The method is shown capable of correcting images reconstructed from sub-optimal probe geometries using only signal domain data without the need for training with ground truth (GT) full-view images.

关键词： Optoacoustics Limited view Artifacts Signal Domain Learning Style Transfer Domain Adaptation

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Derivative compressive sampling with application to phase unwrapping

Derivative compressive sampling with application to phase un...

引用

17th European Signal Processing conference, EUSIPCO 2009

作者： Hosseini, Mahdi S. Michailovich, Oleg v. Department of ECE University of Waterloo 200 University Avenue West Waterloo N2L 3G1 ON Canada

reconstruction of multidimensional signals from the samples of their partial derivatives is known to be an important problem in imaging sciences, with its fields of application including optics, interferometry, computer vision, and remote sensing, just to name a few. Due to the nature of the derivative operator, the above reconstruction problem is generally regarded as ill-posed, which suggests the necessity of using some a priori constraints to render its solution unique and stable. The ill-posed nature of the problem, however, becomes much more conspicuous when the set of data derivatives occurs to be incomplete. In this case, a plausible solution to the problem seems to be provided by the theory of compressive sampling, which looks for solutions that fit the measurements on one hand, and have the sparsest possible representation in a predefined basis, on the other hand. One of the most important questions to be addressed in such a case would be of how much incomplete the data is allowed to be for the reconstruction to remain useful. With this question in mind, the present note proposes a way to augment the standard constraints of compressive sampling by additional constraints related to some natural properties of the partial derivatives. It is shown that the resulting scheme of derivative compressive sampling (DCS) is capable of reliably recovering the signals of interest from much fewer data samples as compared to the standard CS. As an example application, the problem of phase unwrapping is discussed. © EURASIP, 2009.

关键词： Compressed sensing

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Hybrid approach to Bayesian image reconstruction

Hybrid approach to Bayesian image reconstruction

引用

conference on Optical Tomography and Spectrosopy of Tissue v

作者： Zhang, CY Eppstein, MJ Godavarty, A Sevick-Muraca, EM Univ Vermont Dept Comp Sci Burlington VT 05405 USA

ISBN: (纸本)0819447552

The approximate extended Kalman filter (AEKF) has been suggested as an appropriate inverse method for reconstructing fluorescent properties in large tissue samples from frequency domain data containing measurement error. The AEKF is an "optimal" estimator, in that it seeks to minimize the predicted error variances of the estimated optical properties in relation to measurement and system errors. However, due to non-linearities in the recursive estimation process, the updates are actually suboptimal. Furthermore, the computational overhead is large for the full AEKF algorithm when applied to large datasets. In this contribution we developed three hybrid forms of the AEKF algorithm that may improve the performance in frequency domain fluorescence tomography. Numerical results of image reconstruction from actual frequency domain emission data show that one hybrid form of the AEKF outperforms the traditional full AEKF in both image quality and computational efficiency for the two cases tested.

关键词： approximate extended Kalman filter optical tomography fluorescence tomography image reconstruction frequency domain photon migration

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