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36th Annual conference of the European-Association-for-Computer-Graphics

作者： Miandji, Ehsan Kronander, Joel Unger, Jonas Linkoping Univ S-58183 Linkoping Sweden

We present a new compressed sensing framework for reconstruction of incomplete and possibly noisy images and their higher dimensional variants, e.g. animations and light-fields. The algorithm relies on a learning-based basis representation. We train an ensemble of intrinsically two-dimensional (2D) dictionaries that operate locally on a set of 2D patches extracted from the input data. We show that one can convert the problem of 2D sparse signal recovery to an equivalent 1D form, enabling us to utilize a large family of sparse solvers. The proposed framework represents the input signals in a reduced union of subspaces model, while allowing sparsity in each subspace. Such a model leads to a much more sparse representation than widely used methods such as K-SVD. To evaluate our method, we apply it to three different scenarios where the signal dimensionality varies from 2D (images) to 3D (animations) and 4D (light-fields). We show that our method outperforms state-of-the-art algorithms in computer graphics and image processing literature.

关键词： image reconstruction

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Compressive image reconstruction in Reduced Union of Subspaces

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COMPUTER GRAPHICS FORUM 2015年第2期34卷 33-44页

作者： Miandji, Ehsan Kronander, Joel Unger, Jonas Linkoping Univ S-58183 Linkoping Sweden

关键词： Categories and Subject Descriptors (according to ACM CCS) I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism&mdash Color shading shadowing and texture

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image Restoration via Multi-prior Collaboration 1

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12th Asian conference on Computer Vision (ACCV)

作者： Jiang, Feng Zhang, Shengping Zhao, Debin Kung, S. Y. Harbin Inst Technol Sch Comp Sci Harbin 150006 Peoples R China Princeton Univ Sch Elect Engn Princeton NJ 08544 USA

ISBN: (数字)9783319168111

ISBN: (纸本)9783319168111;9783319168104

This paper proposes a novel multi-prior collaboration framework for image restoration. Different from traditional non-reference image restoration methods, a big reference image set is adopted to provide the references and predictions of different popular prior models and accordingly further guide the subsequentmulti-prior collaboration. In particular, the collaboration of multi-prior models is mathematically formulated as a ridge regression problem. Due to expensive computation complexity of handling big reference data, scatter-matrix-based kernel ridge regression is proposed, which achieves high accuracy while low complexity. Additionally, an iterative pursuit is further proposed to obtain refined and robust restoration results. Five popular prior methods are applied to evaluate the effectiveness of the proposed multi-prior collaboration framework. Compared with the state-of-the-art image restoration approaches, the proposed framework improves the restoration performance significantly.

关键词： image reconstruction

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Analysis and accurate reconstruction of incomplete data in X-ray differential phase-contrast computed tomography

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2014年第3期406卷 897-904页

作者： Fu, Jian Tan, Renbo Chen, Liyuan Beijing Univ Aeronaut & Astronaut Res Ctr Digital Radiat Imaging & Biomed Imaging Beijing 100191 Peoples R China

X-ray differential phase-contrast computed tomography (DPC-CT) is a powerful physical and biochemical analysis tool. In practical applications, there are often challenges for DPC-CT due to insufficient data caused by few-view, bad or missing detector channels, or limited scanning angular range. They occur quite frequently because of experimental constraints from imaging hardware, scanning geometry, and the exposure dose delivered to living specimens. In this work, we analyze the influence of incomplete data on DPC-CT image reconstruction. Then, a reconstruction method is developed and investigated for incomplete data DPC-CT. It is based on an algebraic iteration reconstruction technique, which minimizes the image total variation and permits accurate tomographic imaging with less data. This work comprises a numerical study of the method and its experimental verification using a dataset measured at the W2 beamline of the storage ring DORIS iii equipped with a Talbot-Lau interferometer. The numerical and experimental results demonstrate that the presented method can handle incomplete data. It will be of interest for a wide range of DPC-CT applications in medicine, biology, and nondestructive testing.

关键词： incomplete data image reconstruction Tomography Talbot-Lau interferometer X-ray image

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On Compressed Sensing image reconstruction using Multichannel Fusion and Adaptive Filtering

On Compressed Sensing Image Reconstruction using Multichanne...

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International conference on image Processing Theory, Tools and Applications

作者： Sheikh Rafiul Islam Santi P. Maity Ajoy Kumar Ray Neotia Institute of Technology Management and Science Indian Inst. of Engg. Science and Tech.

ISBN: (纸本)9781479986385

Compressed sensing (CS) in imaging is essential in many practical situations and does need suitable algorithm for reconstruction from this highly incomplete observations/measurements. Measurements are also noisy in majority of the cases. This paper addresses CS-reconstruction of images from multiple channel data acquisition with under sampling measurements. An adaptive filtering based stochastic approximation through a recursive system is explored to reconstruct images from CS-multichannel noisy measurements using fusion technique. A simple weighted averaging is done in fusion process where the weights are calculated based on estimation of noise level. A spatial domain adaptive Wiener filter is then used to diminish the noise and reveals new features from the degraded observations. Experimental results show that the proposed multichannel fusion based CS reconstruction scheme outperforms individual channels both in subjective and objective quality measures.

关键词： Compressed sensing Multichannel CS-fusion image fusion Adaptive filtering image reconstruction adaptive filters image reconstruction Compressed sensing image fusion Noise Fusion reactions Fusion, biological FUSION fusion (melting) Wiener filters images

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Digital-Based Engineering Tools for Tailored Design of Medical Implants 5

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5th European conference on Mechanism Science (EUCOMES)

作者： Neto, R. Marques, T. Marta, M. Leal, N. Couto, M. Machado, M. INEGI Inst Engn Mecan & Gestao Ind Oporto Portugal Univ Porto FEUP Fac Engn P-4100 Oporto Portugal Ctr Hosp Sao Joao Oporto Portugal

ISBN: (纸本)9783319094106

With the advances in medicine and surgical treatments and the increase of computational tools and technical aids for supporting surgery, preoperative planning has become even more important and required. This technology relies upon digital tools and helps the clinicians to select the implant, to define its exact location and to establish a task outline for the surgery. Using these computational methods, it is also possible to design medical implants targeting a specific patient or clinical scenario. In this work, a three-step methodology for preoperative planning and design patient-specific implants is presented, namely (i) data acquisition and 3-D reconstruction, (ii) 3-D modelling and (iii) implant design. Two distinct casestudies are considered. The former consisted of a triple fracture of the distal femur, being the second case-study related to a maxillofacial deformity due to cancer removal surgery. For sake of pre-validation of the implant design, plastic prototypes were fabricated by using SL-stereolithography techniques. A tailored design of an implant leads to an enhancement on its biofunctionality, as this novel implant properly fits the extant medical need. Furthermore, the implant customization allows for its correct placement, promoting its fixation and longevity. In cases of osteosynthesis, a personalised design avoids the need of deforming the plates during surgery to fit patient's anatomy.

关键词： Preoperative planning image segmentation 3-D reconstruction Novel design Computer modelling Patient-specific implants SL-stereolithography

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Clinical evaluation of direct 4D whole-body PET parametric imaging with time-of-flight and resolution modeling capabilities

Clinical evaluation of direct 4D whole-body PET parametric i...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Nicolas A. Karakatsanis Abolfazl Mehranian Martin A. Lodge Michael E. Casey Arman Rahmim Habib Zaidi Division of Nuclear Medicine and Molecular Imaging University of Geneva Geneva Switzerland Department of Radiology Johns Hopkins University Baltimore MD USA Siemens Medical Solutions Knoxville TN USA Department of Electrical & Computer Engineering Johns Hopkins University Baltimore MD USA Geneva Neuroscience Centre University of Geneva Geneva Switzerland Department of Nuclear Medicine and Molecular Imaging University of Groningen Groningen Netherlands

Whole-body (WB) PET parametric imaging has recently become clinically feasible with the introduction of multi-bed dynamic acquisition protocols, benefiting from the latest technologies in clinical PET scanners. Currently, Time-of-Flight (TOF) capabilities of modern PET systems allow for more accurate localization of the annihilation position along the line of response (LOR). As a result, TOF can prevent propagation, during image reconstruction, of various resolution degrading factors and noise beyond their origin and across the image space, thus providing i) an inherent correction or contrast recovery mechanism and ii) an effective sensitivity gain relative to non-TOF acquisitions. In addition, the incorporation of the PET system's point spread function (PSF) within the reconstruction system matrix has resulted in i) enhanced contrast and ii) considerably lower image roughness. Recently, we explored the effect of TOF and PSF on WB indirect Patlak imaging. In this work, we systematically investigate the additional benefit of TOF and PSF on clinical studies when reconstructing WB Patlak images directly from projection data. Therefore, we developed a nested direct 4D Patlak WB reconstruction algorithm capable of i) utilizing TOF information, ii) modeling PSF with an effective space-invariant Gaussian resolution kernel and iii) supporting both standard and generalized Patlak analysis. Our clinical evaluation on a set of WB dynamic clinical studies, as acquired with Siemens Biograph mCT TOF scanner, indicated a 15-30% target-to-background (TBR) and contrast-to-noise ratio (CNR) enhancement in all examined regions and for both Patlak methods, when only TOF feature is enabled, with an additional 5-10% improvement when combined with a 4mm FWHM Gaussian PSF kernel. Thus, we have demonstrated the benefits of integrating TOF and PSF features within a clinically adoptable direct 4D WB generalized Patlak reconstruction scheme.

关键词： Positron emission tomography image reconstruction image resolution Protocols Kernel Solid modeling

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INVERSE SURFACELET TRANSFORM FOR image reconstruction WITH PRIOR KNOWLEDGE

INVERSE SURFACELET TRANSFORM FOR IMAGE RECONSTRUCTION WITH P...

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ASME International Design Engineering Technical conferences and Computers and Information in Engineering conference (IDETC/CIE)

作者： Huang, Wei Wang, Yan Rosen, David W. Georgia Inst Technol Sch Mech Engn Atlanta GA 30332 USA

ISBN: (纸本)9780791855850

image reconstruction is the transformation process from some other data forms to image pixels. It can be utilized as a method to retrieve material composition information in materials characterization and design. In our previous work, a so-called surfacelet model was proposed to construct the geometric boundary and internal material distribution of heterogeneous materials at the same time. A surfacelet transform is able to efficiently represent boundary information in images of materials. In this paper, new constrained-conjugate-gradient-based image reconstruction methods are proposed as the inverse surfacelet transform. With geometric constraints on internal boundaries of materials, the proposed method is able to automatically identify the locations and orientations of the internal boundaries based on prior knowledge so as to reconstruct material composition with incomplete data.

关键词： image reconstruction

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reconstruction of Compressive Multispectral Sensing data Using a Multilayered Conditional Random Field Approach 37

Reconstruction of Compressive Multispectral Sensing Data Usi...

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conference on Applications of Digital image Processing XXXVII

作者： Kazemzadeh, Farnoud Shafiee, Mohammad J. Wong, Alexander Clausi, David A. Univ Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781628412444

The prevalence of compressive sensing is continually growing in all facets of imaging science. Compressive sensing allows for the capture and reconstruction of an entire signal from a sparse (undersampled), yet sufficient, set of measurements that is representative of the target being observed. This compressive sensing strategy reduces the duration of the data capture, the size of the acquired data, and the cost of the imaging hardware as well as complexity while preserving the necessary underlying information. Compressive sensing systems require the accompaniment of advanced reconstruction algorithms to reconstruct complete signals from the sparse measurements made. Here, a new reconstruction algorithm is introduced specifically for the reconstruction of compressive multispectral (MS) sensing data that allows for high-quality reconstruction from acquisitions at sub-Nyquist rates. We propose a multilayered conditional random field (MCRF) model, which extends upon the CRF model by incorporating two joint layers of certainty and estimated states. The proposed algorithm treats the reconstruction of each spectral channel as a MCRF given the sparse MS measurements. Since the observations are incomplete, the MCRF incorporates an extra layer determining the certainty of the measurements. The proposed MCRF approach was evaluated using simulated compressive MS data acquisitions, and is shown to enable fast acquisition of MS sensing data with reduced imaging hardware cost and complexity.

关键词： Compressive sensing: multispectral reconstruction image reconstruction conditional random field

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The application of compressed sensing method in photoacoustic image reconstruction 4

The application of compressed sensing method in photoacousti...

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2014 4th IEEE International conference on Information Science and Technology, ICIST 2014

作者： Hu, Danfeng Wang, Jiajun Fang, Erxi Zhou, Wei Zhou, Yue School of Electronics and Information Soochow University Suzhou Jiangsu China

ISBN: (纸本)9781479948086

Full-scanned photoacoustic data of the sample are needed to achieve better quality of the reconstructed photoacoustic image with filtered back projection algorithm. However, such a requirement is usually difficult to be satisfied due to the restrictions of the hardware conditions and spatial size. To tackle such a problem, a compressed sensing based method was proposed to recover the full-scanned photoacoustic data from the incomplete measurements. The results from the numerical simulation demonstrates that both the mean squared error and the peak signal-to-noise ratio(PSNR) of the image can significantly improved with our proposed method. © 2014 IEEE.

关键词： image reconstruction

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