• Journal of the Korean Physical Society
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• v.73 no.12
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• pp.1845-1848
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• 2018

We introduce a process for optically reconstructing full-color holographic images recorded by optical scanning holography. A complex RGB-color hologram was recorded and converted into a binary hologram using a direct binary search (DBS) algorithm. The generated binary hologram was then optically reconstructed using a spatial light modulator. The discrepancies between the reconstructed object sizes and colors due to chromatic aberration were corrected by adjusting the reconstruction parameters in the DBS algorithm. To the best of our knowledge, this represents the first optical reconstruction of a full-color hologram recorded by optical scanning holography.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.150-150
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• 2000

We as modal parameter estimation technique by developing a residual based system reconstruction and using the system matrix coordinate transformation. The modal parameters can be estimated from and residues of the system transfer functions expressed in modal coordinate basis, derived from the state space system matrices. However, for modal parameter estimation of multivariable and order structural systems over broad frequency bands, this non-iterative algorithm gives high accuracy in the natural fre- and damping ratios. From vibration tests on cross-ply and angle-ply composite laminates, the natural frequencies and damping ratios on be estimated using tile coordinates of the structural system reconstructed fro the experimental frequency response. These results are compared with those of finite element analysis and single-degree-of-freedom curve-fitting.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.527-527
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• 2000

Wean modal parameter estiimation technique by developing a residual based system reconstruction and using the system matrix coordinate transformation. The modal parameters can be estimated from and residues of the system transfer functions expressed in modal coordinate basis, derived from the state space system matrices. However, for modal parameter estimation of mllltivariable and order structural systems over broad frequency bands, this non-iterative algorithm gives high accuracy in the natural fre and damping ratios. From vibration tests on cross-ply and angle-ply composite laminates, the natural frequencies and damping ratios can be estimated using the coordinates of the structural system reconstructed from the experimental frequency response. These results are compared with those of finite element analysis and single-degree-of-freedom curve-fitting..

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.199-207
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• 2021

This paper deals with accurate image reconstruction of gamma camera using a coded-aperture mask based on pixel-type CsI(Tl) scintillator coupled with silicon photomultipliers (SiPMs) array. Coded-aperture imaging (CAI) system typically has a smaller effective viewing angle than Compton camera. Thus, if the position of the gamma source to be searched is out of the fully-coded field-of-view (FCFOV) region of the CAI system, artifacts can be generated when the image is reconstructed by using the conventional cross-correlation (CC) method. In this work, we propose an effective method for more accurate reconstruction in CAI considering the source distribution of partially-coded field-of-view (PCFOV) in the reconstruction in attempt to overcome this drawback. We employed an iterative algorithm based on compressed-sensing (CS) and compared the reconstruction quality with that of the CC algorithm. Both algorithms were implemented and performed a systematic Monte Carlo simulation to demonstrate the possiblilty of the proposed method. The reconstructed image qualities were quantitatively evaluated in sense of the root mean square error (RMSE) and the peak signal-to-noise ratio (PSNR). Our simulation results indicate that the proposed method provides more accurate location information of the simulated gamma source than the CC-based method.

• Korean Journal of Radiology
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• v.23 no.11
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• pp.1044-1054
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• 2022

Objective: This study aimed to investigate whether a deep learning reconstruction (DLR) method improves the image quality, stent evaluation, and visibility of the valve apparatus in coronary computed tomography angiography (CCTA) when compared with filtered back projection (FBP) and hybrid iterative reconstruction (IR) methods. Materials and Methods: CCTA images of 51 patients (mean age ± standard deviation [SD], 63.9 ± 9.8 years, 36 male) who underwent examination at a single institution were reconstructed using DLR, FBP, and hybrid IR methods and reviewed. CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and stent evaluation, including 10%-90% edge rise slope (ERS) and 10%-90% edge rise distance (ERD), were measured. Quantitative data are summarized as the mean ± SD. The subjective visual scores (1 for worst -5 for best) of the images were obtained for the following: overall image quality, image noise, and appearance of stent, vessel, and aortic and tricuspid valve apparatus (annulus, leaflets, papillary muscles, and chordae tendineae). These parameters were compared between the DLR, FBP, and hybrid IR methods. Results: DLR provided higher Hounsfield unit (HU) values in the aorta and similar attenuation in the fat and muscle compared with FBP and hybrid IR. The image noise in HU was significantly lower in DLR (12.6 ± 2.2) than in hybrid IR (24.2 ± 3.0) and FBP (54.2 ± 9.5) (p < 0.001). The SNR and CNR were significantly higher in the DLR group than in the FBP and hybrid IR groups (p < 0.001). In the coronary stent, the mean value of ERS was significantly higher in DLR (1260.4 ± 242.5 HU/mm) than that of FBP (801.9 ± 170.7 HU/mm) and hybrid IR (641.9 ± 112.0 HU/mm). The mean value of ERD was measured as 0.8 ± 0.1 mm for DLR while it was 1.1 ± 0.2 mm for FBP and 1.1 ± 0.2 mm for hybrid IR. The subjective visual scores were higher in the DLR than in the images reconstructed with FBP and hybrid IR. Conclusion: DLR reconstruction provided better images than FBP and hybrid IR reconstruction.

• Journal of the Korean Society of Radiology
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• v.15 no.7
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• pp.1065-1072
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• 2021

In Computed Tomography, abdominal examination showed the highest proportion of use, and effort of reducing the radiation dose is required. Recently introduced Iterative Reconstruction(IR) is repetitive reconstruction technique of Computed Tomography. SIEMENS' IR, ADMIRE and GE's IR, ASIR-V, were used in this examination. Noise, % Contrast, and High contrast resolution were measured by using ACR phantom for image quality evaluation. In addition, CTDI_vol and DLP displayed in the CT device were used for dose evaluation. When FBP and IR were compared, stage 2 to stage 5 of ADMIRE and 10, 30, 50, 70, and 90% of ASIR-V were applied, noise could be reduced from a minimum of 0.46 to a maximum of 2.38 in ADMIRE compared to FBP, and noise from a minimum of 0.51 to a maximum of 2.5 in ASIR-V compared to FBP. Also, % Contrast and High contrast resolution of FBP and IR were no statistical difference. When IR was used for abdominal CT examination, the radiation dose of ADMIRE is reduced by 25.39% compared to the radiation dose of FBP. Also, the radiation dose of ASIR-V is reduced by 16.61% compared to the radiation dose of FBP. In conclusion, it is believed that if IR is applied during abdominal CT examination, the radiation dose can be reduced without deteriorating the image quality.

• Current Optics and Photonics
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• v.8 no.3
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• pp.246-258
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• 2024

In aerospace and energy engineering, the reconstruction of three-dimensional (3D) temperature distributions is crucial. Traditional methods like algebraic iterative reconstruction and filtered back-projection depend on voxel division for resolution. Our algorithm, blending deep learning with computer graphics rendering, converts 2D projections into light rays for uniform sampling, using a fully connected neural network to depict the 3D temperature field. Although effective in capturing internal details, it demands multiple cameras for varied angle projections, increasing cost and computational needs. We assess the impact of camera number on reconstruction accuracy and efficiency, conducting butane-flame simulations with different camera setups (6 to 18 cameras). The results show improved accuracy with more cameras, with 12 cameras achieving optimal computational efficiency (1.263) and low error rates. Verification experiments with 9, 12, and 15 cameras, using thermocouples, confirm that the 12-camera setup as the best, balancing efficiency and accuracy. This offers a feasible, cost-effective solution for real-world applications like engine testing and environmental monitoring, improving accuracy and resource management in temperature measurement.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.297-303
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• 2023

Pediatric Computed Tomography (CT) examinations can often result in exam failures or the need for frequent retests due to the difficulty of cooperation from young patients. Deep Learning Image Reconstruction (DLIR) methods offer the potential to obtain diagnostically valuable images while reducing the retest rate in CT examinations of pediatric patients with high radiation sensitivity. In this study, we investigated the possibility of applying DLIR to reduce artifacts caused by respiration or motion and obtain clinically useful images in pediatric chest CT examinations. Retrospective analysis was conducted on chest CT examination data of 43 children under the age of 7 from P Hospital in Gyeongsangnam-do. The images reconstructed using Filtered Back Projection (FBP), Adaptive Statistical Iterative Reconstruction (ASIR-50), and the deep learning algorithm TrueFidelity-Middle (TF-M) were compared. Regions of interest (ROI) were drawn on the right ascending aorta (AA) and back muscle (BM) in contrast-enhanced chest images, and noise (standard deviation, SD) was measured using Hounsfield units (HU) in each image. Statistical analysis was performed using SPSS (ver. 22.0), analyzing the mean values of the three measurements with one-way analysis of variance (ANOVA). The results showed that the SD values for AA were FBP=25.65±3.75, ASIR-50=19.08±3.93, and TF-M=17.05±4.45 (F=66.72, p=0.00), while the SD values for BM were FBP=26.64±3.81, ASIR-50=19.19±3.37, and TF-M=19.87±4.25 (F=49.54, p=0.00). Post-hoc tests revealed significant differences among the three groups. DLIR using TF-M demonstrated significantly lower noise values compared to conventional reconstruction methods. Therefore, the application of the deep learning algorithm TrueFidelity-Middle (TF-M) is expected to be clinically valuable in pediatric chest CT examinations by reducing the degradation of image quality caused by respiration or motion.

• The Journal of the Acoustical Society of Korea
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• v.11 no.2
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• pp.5-14
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• 1992

In the fields of astronomy, communication, X-ray crystallography, and engineering, it is a very important and useful fact that the original signal can be reconstructed from a partial information, only spectral magnitude or phase, of the signal. In this paper, we proposed a modified iterative algorithm to solve the Magnitude Retrieval Problem (MRP) for 1-D, 2-D signals. In oder to accelerate the convergence rate, the unit constant initial function which is used in the references is replaced by the exponential initial function for the modified adaptive iterative method. As a result, MRP with 1-D signal and low-pass detail image is significantly enhanced from an iterative convergence rate and a computer storage memory points of view.

• Journal of Korea Multimedia Society
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• v.14 no.8
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• pp.1000-1009
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• 2011

Image interpolation is a technique which estimates the non-allocated pixel values on image scale-transform. It requires minimum computational complexity and minimum image quality degradation on the interpolated resultant images. In this paper we propose an image interpolation method using iterative error estimation. The proposed method consists of the following five steps: loss-information computational step, loss-information estimation step, loss-information application step, error computation step, and error application step. The experimental results obtained show that the average PSNR is increased by 3.3dB, subjective image quality is enhanced and the minimum computation complexity is decreased by 83%. The proposed image interpolation algorithm may be helpful in various applications such as image reconstruction and enlargement.