• Journal of radiological science and technology
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• v.40 no.2
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• pp.261-267
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• 2017

The study has attempted to evaluate and compare the image evaluation and exposure dose by respectively applying filter back projection (FBP), the existing test method, and adaptive statistical iterative reconstruction (ASIR) with different values of tube voltage during the low dose computed tomography (LDCT). With the image reconstruction method as basis, chest phantom was utilized with the FBP and ASIR set at 10%, 20% respectively, and the change of tube voltage (100 kVp, 120 kVp). For image evaluation, back ground noise, signal-noise ratio (SNR) and contrast-noise ratio (CNR) were measured, and, for dose assessment, CTDIvol and DLP were measured respectively. In terms of image evaluation, there was significant difference in ascending aorta (AA) SNR and inpraspinatus muscle (IM) SNR with the different amount of tube voltage (p < 0.05). In terms of CTDIvol, the measured values with the same tube voltage of 120 kVp were 2.6 mGy with no-ASIR and 2.17 mGy with 20%-ASIR respectively, decreased by 0.43 mGy, and the values with 100 kVp were 1.61 mGy with no-ASIR and 1.34 mGy with 20%-ASIR, decreased by 0.27 mGy. In terms of DLP, the measured values with 120 kVp were $103.21mGy{\cdot}cm$ with no-ASIR and $85.94mGy{\cdot}cm$ with 20%-ASIR, decreased by $17.27mGy{\cdot}cm$ (about 16.7%), and the values with 100 kVp were $63.84mGy{\cdot}cm$ with no-ASIR and $53.25mGy{\cdot}cm$ with 20%-ASIR, a decrease by $10.62mGy{\cdot}cm$ (about 16.7%). At lower tube voltage, the rate of dose significantly decreased, but the negative effects on image evaluation was shown due to the increase of noise.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.275-280
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• 1994

In the case of the image reconstruction from unknown projection data such as imaging the object with opaque obstructions, conventional reconstruction algorithms may reconstruct a degraded image. In this paper, a new method for the estimation of the unknown projection data based on known projection data and the bandwidth of projection data is proposed. The proposed method successfully estimates the unknown projection data through iterative transformation between projection space and frequency space using the known projection data and the bandwidth of the projection data. Computer simulation shows that the proposed method significantly improves image quality and convergence behavior over conventional algorithms. In addition, the proposed method is successfully applied to ultrasound attenuation CT using a sponge phantom.

• Journal of applied mathematics & informatics
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• v.29 no.3_4
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• pp.697-712
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• 2011

We present in this paper two versions of a general correction procedure applied to a classical linear iterative method. This gives us the possibility, under certain assumptions, to obtain an extension of it to inconsistent linear least-squares problems. We prove that some well known extended projection type algorithms from image reconstruction in computerized tomography fit into one or the other of these general versions and are derived as particular cases of them. We also present some numerical experiments on two phantoms widely used in image reconstruction literature. The experiments show the importance of these extension procedures, reflected in the quality of reconstructed images.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.737-741
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• 2009

This paper addresses the factorization method to estimate the projective structure of a scene from feature (points) correspondences over images with occlusions. We propose both a column and a row space approaches to estimate the depth parameter using the subspace constraints. The projective depth parameters are estimated by maximizing projection onto the subspace based either on the Joint Projection matrix (JPM) or on the the Joint Structure matrix (JSM). We perform the maximization over significant observation and employ Tardif's Camera Basis Constraints (CBC) method for the matrix factorization, thus the missing data problem can be overcome. The depth estimation and the matrix factorization alternate until convergence is reached. Result of Experiments on both real and synthetic image sequences has confirmed the effectiveness of our proposed method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.103-107
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• 2009

This paper presents how to minimize the second-order cone programming problem occurring in the 3D reconstruction of multiple views. The $L_{\infty}$-norm minimization is done by a series of the minimization of the maximum infeasibility. Since the problem has many inequality constraints, we have to adopt methods of the interior point algorithm, in which the inequalities are sequentially approximated by log-barrier functions. An initial feasible solution is found easily by the construction of the problem. Actual computing is done by an iterative Newton-style update. When we apply the interior point method to the problem of reconstructing the structure and motion, every Newton update requires to solve a very large system of linear equations. We show that the sparse bundle-adjustment technique can be utilized in the same way during the Newton update, and therefore we obtain a very efficient computation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.638-643
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• 2009

A computer-generated hologram(CGH) is made for three-dimensional image reconstruction of a virtual object which is a difficult to irradiate the laser light directly. One of the adverse effect factors is quantization of wave front computed by program when a computer-generated hologram is made. Amplitude element is not considered in Kinoform, it needs processing to reduce noise or false image. So several investigation was reported that the improvement of reconstructed image of Kinoform. Means to calculate the most suitable complex amplitude distribution are iterative algorithm, simulated annealing algorithm and genetic Algorithm. Error diffusion method reconstructed to separate the object as for the noise that originated in the quantization error. So it is efficient method to obtain high quality image with not many processing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4463-4482
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• 2020

Mesh model generated from 3D reconstruction usually comes with lots of noise, which challenges the performance and robustness of mesh simplification approaches. To overcome this problem, we present a novel method for mesh simplification which could preserve structure and improve the accuracy. Our algorithm considers both the planar structures and linear features. In the preprocessing step, it automatically detects a set of planar structures through an iterative diffusion approach based on Region Seed Growing algorithm; then robust linear features of the mesh model are extracted by exploiting image information and planar structures jointly; finally we simplify the mesh model with plane constraint QEM and linear feature preserving strategies. The proposed method can overcome the known problem that current simplification methods usually degrade the structural characteristics, especially when the decimation is extreme. Our experimental results demonstrate that the proposed method, compared to other simplification algorithms, can effectively improve the quality of mesh and yield an increased robustness on noisy input mesh.

• Current Optics and Photonics
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• v.6 no.1
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• pp.1-9
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• 2022

This paper proposes a robust, simple zonal wavefront-estimation method in a grid sampling model. More slopes are added to the integral equation of the algorithm to improve the accuracy and convergence rate of this approach, especially for higher-order optical aberrations. The Taylor theorem is applied to clarify the mathematical description of the remaining error in the proposed method. Several numerical simulations are conducted to ensure the performance and improvement in comparison to the Southwell and previous algorithm. An experiment is also conducted according to deflectometry output and the results are verified using a reference measured with a stylus system.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.240-242
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• 1988

This paper proposes an improved ART (Algebraic Reconstruction technique) algorithm. This algorithm is an iterative one with iteration sequence criteria based on the discrepancy between measurement and pseudo-projection data. The simulation result using the proposed algorithm shows a significant improvement in convergency rate over the conventional ART algorithm.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.770-783
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• 2012

In this paper, we propose a novel structure recovery algorithm in the projective space using image feature points. We use normalized image feature coordinates for the numerical stability. To acquire an initial value of the structure and motion, we decompose the scaled measurement matrix using the singular value decomposition. When recovering structure and motion in projective space, we introduce matrix decomposition constraints. In the reconstruction procedure, a nonlinear iterative optimization technique is used. Experimental results showed that the proposed method provides proper accuracy and the error deviation is small.