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image reconstruction by Prioritized Incremental Normalized Convolution 1

17th Scandinavian conference on image Analysis (SCIA)

作者： Landstrom, Anders Nellros, Frida Jonsson, Hakan Thurley, Matthew Lulea Univ Technol Dept Comp Sci Elect & Space Engn S-97187 Lulea Sweden

ISBN: (数字)9783642212277

ISBN: (纸本)9783642212277

A priority-based method for pixel reconstruction and incremental hole filling in incomplete images and 3D surface data is presented. The method is primarily intended for reconstruction of occluded areas in 3D surfaces and makes use of a novel prioritizing scheme, based on a pixelwise defined confidence measure, that determines the order in which pixels are iteratively reconstructed. The actual reconstruction of individual pixels is performed by interpolation using normalized convolution. The presented approach has been applied to the problem of reconstructing 3D surface data of a rock pile as well as randomly sampled image data. It is concluded that the method is not optimal in the latter case, but the results show an improvement to ordinary normalized convolution when applied to the rock data and are in this case comparable to those obtained from normalized convolution using adaptive neighborhood sizes.

关键词： image reconstruction hole filling normalized convolution

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Quantitative and Qualitative Comparison of SAR images from incomplete Measurements Using Compressed Sensing and Nonuniform FFT

Quantitative and Qualitative Comparison of SAR Images from I...

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IEEE Radar conference (RADAR)

作者： Kajbaf, Hamed Case, Joseph T. Zheng, Yahong Rosa Kharkovsky, Sergey Zoughi, Reza Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA

ISBN: (纸本)9781424489022

In this paper the performance of two wideband synthetic aperture radar (SAR) imaging methods from incomplete data sets are compared quantitatively and qualitatively. The first approach uses nonuniform fast Fourier transform (NUFFT) SAR to form images from nonuniform spatial and frequency data points. The second approach benefits from the emerging compressed sensing (CS) methodology to recover raw data from undersampled measurements. The results of our experimental tests show that CS has a better performance in terms of error and image contrast while NUFFT SAR has lower computational complexity.

关键词： Fast Fourier transforms image reconstruction Imaging Measurement Probes Signal to noise ratio Three dimensional displays compressed sensing fast Fourier transform fast Fourier transforms incomplete measurements nonuniform FFT radar imaging synthetic apert

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SENSITIvITY TO ERROR OF THE TRUNCATED HILBERT TRANSFORM TECHNIQUE FOR INTERIOR reconstruction

SENSITIVITY TO ERROR OF THE TRUNCATED HILBERT TRANSFORM TECH...

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18th IEEE International conference on image Processing (ICIP)

作者： Opie, A. M. T. Bones, P. J. Univ Canterbury Dept Elect & Comp Engn Christchurch 1 New Zealand

ISBN: (纸本)9781457713033

Historically, computed tomography reconstructions from truncated projection data have been considered non-unique. However, several recent results suggest that if the density of the object is known on some small region within the region of interest (ROI) then a unique and stable reconstruction of the complete ROI may be possible. Unfortunately, prior knowledge of the exact density of an object being scanned is uncommon, and as such an experimentally determined estimate must be used in its place. This estimate will naturally contain errors. We have performed several reconstruction simulations to establish the sensitivity of the proposed algorithm to errors in the prior knowledge. Our results suggest that the most important element of the prior knowledge is its mean value, while perturbations of the details cause fewer problems in the reconstructed images.

关键词： Computed tomography incomplete data sensitivity to error

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Limited data image reconstruction in optoacoustic tomography by constrained, total variation minimization

Limited data image reconstruction in optoacoustic tomography...

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conference on Photons Plus Ultrasound - Imaging and Sensing

作者： Wang, Kun Sidky, Emil Y. Anastasio, Mark A. Oraevsky, Alexander A. Pan, Xiaochuan Washington Univ Dept Biomed Engn St Louis MO 63130 USA Univ Chicago Dept Radiol MC 2026 Chicago IL 60637 USA TomoWave Labs Houston TX 77057 USA

ISBN: (纸本)9780819484369

Optoacoustic Tomography (OAT) is an emerging hybrid imaging technique with great potential for a wide range of biomedical imaging applications. Assuming point-like transducers, analytic algorithms are available for image reconstruction, but they are applicable only when the measured data are densely sampled on an aperture that encloses the object. In many cases of practical interest, however, measurements may be limited in number and are acquired on an incomplete aperture. Total variation (Tv) minimization has been proved to be a powerful tool for limited-data reconstruction. However, most previous studies of limited-data OAT were based on an approximate imaging model that assumed point-like transducers, which limits the improvements on the reconstructed OAT image quality. In this work, we develop and investigate an iterative reconstruction algorithm incorporating ultrasonic transducer properties applicable for limited-data OAT. The algorithm is based on the minimization of the image Tv subject to a data consistency condition, and is conceptually and mathematically distinct from classic iterative reconstruction algorithms. Preliminary computer-simulation studies are conducted to investigate the proposed algorithm. These studies reveal that the constrained, total variation minimization algorithm can yield accurate reconstructions in many limited-data applications where classic algorithms do not perform well.

关键词： optoacoustic tomography photoacoustic tomography iterative image reconstruction total variation minimization

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Brachytherapy seed reconstruction with joint-encoded C-arm single-axis rotation and motion compensation

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MEDICAL image ANALYSIS 2011年第5期15卷 760-771页

作者： Dehghan, Ehsan Jain, Ameet K. Moradi, Mehdi Wen, Xu Morris, W. James Salcudean, Septimiu E. Gichtinger, Gabor Queens Univ Sch Comp Kingston ON K7L 3N6 Canada Philips Res N Amer Briarcliff Manor NY USA Univ British Columbia Elect & Comp Engn Dept Vancouver BC V5Z 1M9 Canada British Columbia Canc Agcy Vancouver Canc Ctr Vancouver BC V5Z 4E6 Canada

C-arm fluoroscopy images are frequently used for qualitative assessment of prostate brachytherapy. Three-dimensional seed reconstruction from C-arm images is necessary for intraoperative dosimetry and quantitative assessment. Seed reconstruction requires accurately known C-arm poses. We propose to measure the C-arm rotation angles and computationally compensate for inevitable C-arm motion to compute the pose. We compensate the translational motions of a C-arm, such as oscillation, sagging and wheel motion using a three-level optimization algorithm and obviate the need for full pose tracking using external trackers or fiducials. We validated our approach on simulated and 100 clinical data sets from 10 patients and gained on average, a seed matching rate of 98.5%, projection error of 0.33 mm (STD = 0.21 mm) and computation time of 19.8 s per patient, which must be considered as clinically excellent results. We also show that without motion compensation the reconstruction is likely to fail. (C) 2011 Elsevier B.v. All rights reserved.

关键词： Brachytherapy Prostate cancer Seed reconstruction C-arm Motion compensation

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PET image reconstruction from Finite Linogram via Direct Fourier and Logarithmic Barrier Method

PET Image Reconstruction from Finite Linogram via Direct Fou...

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IEEE Nuclear Science Symposium/Medical Imaging conference (NSS/MIC)/18th International Workshop on Room-Temperature Semiconductor X-Ray and Gamma-Ray Detectors

作者： Deng, Zhenzhou Xie, Qingguo Duan, Zhiwen Wuhan Natl Lab Optoelect Wuhan Hubei Peoples R China

ISBN: (纸本)9781467301206

Dual-head PET (Positron Emission Tomography) and PEM (Positron Emission Mammography) are encountered in many biological imaging and medical diagnosis applications. Since the data produced by flat-panel detectors are more closed to the linogram, and rebinning to the sinogram causes errors of the interpolation, image reconstruction based on the linogram is superior to keep the feature of the raw data for dual-head. Length of the linogram produced by actual PET or PEM systems is finite in practice. The projection views of the data are incomplete, and then the finite linogram reconstruction is a limited-view problem in mathematics. Conventional algorithms cannot achieve the exact reconstruction of the limited-view problem. In this work, we propose a half-analytic and half-iterative method named DF/LBM (Direct Fourier and Logarithmic Barrier Method) to solve the limited-view problem. The least square solution is obtained via DFM (Direct Fourier Method), and then a convex optimization method named logarithmic barrier method is employed to correct the least square solution. The substance of the convex optimization is defining the components in the null-space to satisfy some prior information, as the least square solution has no components in the null-space. Applying this method to Hoffman brain phantom, the artifact generated by the least square solution is eliminated, and PSNR is 90.2112, 94.8699 and 99.0507 when the tangent of the half allowable angle is 1.0, 1.5 and 2.0 respectively.

关键词： Barrier Contraception image reconstruction Logarithmic Finite Fourier sinogram Convex function optimization method

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Advanced algorithms for identifying targets from a three-dimensional reconstruction of sparse 3D Ladar data

Advanced algorithms for identifying targets from a three-dim...

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conference on Optical Complex Systems (OCS)

作者： Berechet, Ion Berginc, Gerard SISPIA SARL 18 Allee Henri Dunant F-94300 Vincennes France THALES OPTRONIQUE F-94300 Vincennes France

ISBN: (纸本)9780819487988

There is a considerable interest in the development of new optical imaging systems that are able to give three-dimensional images. Potential applications range across medical imaging, surveillance and robotic vision. Identifying targets or objects concealed by foliage or camouflage is a critical requirement for operations in public safety, law enforcement and defense. The most promising techniques for these tasks are 3D laser imaging techniques. Their principles are to use movable light sources and detectors to collect information on laser scattering and to reconstruct the 3D objects of interest. 3D reconstruction algorithm is a major component in these optical systems for identification of camouflaged objects. But 3D reconstruction must take into account sparse collected data i.e. concealed objects and reconstruction algorithms must solve a complex multi-parameter inverse problem. Therefore the inverse problem of recovering the surface three-dimensional shape function from intensity data is more challenging. The objective of our paper is to present a new algorithmic approach for the generation of 3D surface data from 3D point clouds corresponding to reconstruction algorithm. This algorithmic approach is based on research of automatic minimization of an energy function associated with a sparse structure of 3D points. The role of this type of algorithmic data-driving process is to complete the incomplete 3D image at satisfactory levels for reliable identification of concealed objects.

关键词： 3D Ladar foliage penetration data-driving process energy minimum incomplete area completion point cloud surface generation

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image decoding optimization based on compressive sensing

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2011年第5期236卷 812-818页

作者： Zhang, Zhen Shi, Yunhui Kong, Dehui Ding, Wenpeng Yin, Baocai Beijing Univ Technol Coll Comp Sci & Technol Beijing Municipal Key Lab Multimedia & Intelligen Beijing 100124 Peoples R China

Transform-based image codec follows the basic principle: the reconstructed quality is decided by the quantization level. Compressive sensing (CS) breaks the limit and states that sparse signals can be perfectly recovered from incomplete or even corrupted information by solving convex optimization. Under the same acquisition of images, if images are represented sparsely enough, they can be reconstructed more accurately by CS recovery than inverse transform. So, in this paper, we utilize a modified Tv operator to enhance image sparse representation and reconstruction accuracy, and we acquire image information from transform coefficients corrupted by quantization noise. We can reconstruct the images by CS recovery instead of inverse transform. A CS-based JPEG decoding scheme is obtained and experimental results demonstrate that the proposed methods significantly improve the PSNR and visual quality of reconstructed images compared with original JPEG decoder. (C) 2011 Elsevier B.v. All rights reserved.

关键词： Compressive sensing image decoding Total variation SOCP

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A Practical Updating Background Method in Moving Target Detection

A Practical Updating Background Method in Moving Target Dete...

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International conference on Consumer Electronics, Communications and Networks

作者： Junfang Song Cao Huang Ru Xue School of Information Engineering Tibet Nationalities Institute

ISBN: (纸本)9781457714146

Background subtraction is a powerful mechanism typically for segmenting moving regions in image sequences taken from a data acquisition system. It compares each new image to a model of the scene background image. In this paper, we present a novel method for the reconstruction of background image in background subtraction. This method firstly calculates the difference of two neighboring images, then deals with this result to determine whether the background image needs to be updated and finally reconstructs the background image by a simple weight formula. The experiment result shows that the proposed method could update background image quickly and timely in the scene of traffic flow monitoring. Using this method we can get a more accurate background, which is conducive to the segmentation of the target.

关键词： Traffic flow monitoring Background subtraction Moving target detection Background image updating

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A novel hybrid compressed sensing image reconstrcution method

A novel hybrid compressed sensing image reconstrcution metho...

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International conference on Electronics and Optoelectronics

作者： Wang, Shanshan Liu, Qiegen Luo, Jianhua Zhu, Yuemin College of Life Science and Technology Shanghai Jiaotong University Shanghai China CREATIS CNRS UMR 5220 University of Lyon Villeurbanne France

ISBN: (纸本)9781612842738

In this paper, a hybrid approach of image reconstruction from highly incomplete data is introduced. The method is a weighted recursive filtering procedure. At each iteration, random noise is first injected in the unknown portion of the spectrum, and then a reweighted denoising filter consisting of Block Matching 3D (BM3D) filter and multiscale L0-continuation filter is exploited to attenuate the noise in the image domain and reveal new features and details, finally those new features are projected onto the unknown portion of the spectrum to update the K-space data. The proposed method avoids local solutions and recovers the features and details of the image efficiently by utilizing advantages of both filters. The experimental results on both simulated and real images consistently demonstrate that the proposed approach can efficiently reconstruct the image with high image quality. © 2011 IEEE.

关键词： image denoising

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