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International conference on image Processing Theory Tools and Applications

作者： Islam, Sheikh Rafiul Maity, Santi P. Ray, Ajoy Kumar Neotia Inst Technol Management & Sci 24 Pgs S Amira 743368 WB India Indian Inst Engn Sci & Tech Howrah 711103 WB India

ISBN: (纸本)9781479986378

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

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Portability of TV-Regularized reconstruction Parameters to Varying data Sets

Portability of TV-Regularized Reconstruction Parameters to V...

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conference on image Processing for Medicine (BVM)

作者： Amrehn, Mario Maier, Andreas Dennerlein, Frank Hornegger, Joachim FAU Erlangen Nurnberg Pattern Recognit Lab Erlangen Germany Erlangen Grad Sch Adv Opt Technol SAOT Erlangen Germany

ISBN: (纸本)9783662462249;9783662462232

In C-arm computed tomography there are certain constraints due to the data acquisition process which can cause limited raw data. The reconstructed image's quality may significantly decrease depending on these constraints. To compensate for severely under-sampled projection data during reconstruction, special algorithms have to be utilized, more robust to such ill-posed problems. In the past few years it has been shown that reconstruction algorithms based on the theory of compressed sensing are able to handle incomplete data sets quite well. In this paper, the iterative iTV reconstruction method by Ludwig Ritschl et al. is analyzed regarding it's elimination capabilities of image artifacts caused by incomplete raw data with respect to the settings of it's various parameters. The evaluation of iTV and the data dependency of iterative reconstruction's parameters is conducted in two stages. First, projection data with severe angular under-sampling is acquired using an analytical phantom. Proper reconstruction parameters are selected by analyzing the reconstruction results from a set of proposed parameters. In a second step multiple phantom data sets are acquired with limited angle geometry and a small number of projections. The iTV reconstructions of these data sets are compared to short-scan FDK and SART reconstruction results, highlighting the distinct data dependence of the iTV reconstruction parameters.

关键词： Phantoms

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Projection Classification Based Iterative Algorithm 3

Projection Classification Based Iterative Algorithm

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3rd International conference on Manufacturing, Optimization, Industrial and Material Engineering (MOIME)

作者： Zhang, Ruiqiu Li, Chen Gao, Wenhua S China Univ Technol Sch Design Guangzhou 510640 Guangdong Peoples R China S China Univ Technol Sch Math Guangzhou 510640 Guangdong Peoples R China

Iterative algorithm has good performance as it does not need complete projection data in 3D image reconstruction area. It is possible to be applied in BGA based solder joints inspection but with low convergence speed which usually acts with x-ray Laminography that has a worse reconstruction image compared to the former one. This paper explores to apply one projection classification based method which tries to separate the object to three parts, i.e. solute, solution and air, and suppose that the reconstruction speed decrease from solution to two other parts on both side lineally. And then SART and CAV algorithms are improved under the proposed idea. Simulation experiment result with incomplete projection images indicates the fast convergence speed of the improved iterative algorithms and the effectiveness of the proposed method. Less the projection images, more the superiority is also founded.

关键词： image reconstruction

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Region-Of-Interest tomography with noisy projection data

Region-Of-Interest tomography with noisy projection data

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Proceedings of the 2014 International conference on Medicine Sciences and Bioengineering, ICMSB 2014

作者： Ye, Renzhen Tang, Yi College of Sciences Huazhong Agricultural University WuhanHubei China School of Mathematics and Computer Science Yunnan Minzu University KunmingYunnan China

ISBN: (纸本)9781138026841

In Region-Of-Interest(ROI) tomography, image reconstruction is an ill-conditioned inverse problem due to the presence of additive noise and incomplete projection data. To improve reconstructed image quality, this paper is aimed at reconstructing a small portion of an object from noisy observations of its projections sampled in and near the ROI. The proposed method can divided into two steps to reconstruct ROI of an object from a set of its noisy projection lines that passed through ROI. First, we introduce filter matrices to filter noisy projection data in multiresolution local tomography. Second, we apply total variation(TV) methods to make reconstructed image deblurring. Experimental results show that the proposed method outperforms state-of-the-art method. © 2015 Taylor & Francis Group, London.

关键词： Tomography

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Compressive image reconstruction in Reduced Union of Subspaces

Compressive Image Reconstruction in Reduced Union of Subspac...

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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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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

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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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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