• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6A
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• pp.610-617
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• 2002

In this paper, a new intermediate view reconstruction technique by using a disparity estimation method based-on the adaptive matching window size is proposed. In the proposed method, once the feature values are extracted from the input stereo image, then the matching window size for the intermediate view reconstruction is adaptively selected in accordance with the magnitude of this feature values. That is, coarse matching is performed in the region having smaller feature values while accurate matching is carried out in the region having larger feature values by comparing with the predetermined threshold value. Accordingly, this new approach is not only able to reduce the mismatching probability of the disparity vector mostly happened in the accurate disparity estimation with a small matching window size, but is also able to reduce the blocking effect occurred in the disparity estimation with a large matching window size. Some experimental results on the 'Parts' and 'Piano' images show that the proposed method improves the PSNR about 2.32∼4.16dB and reduces the execution time to about 39.34∼65.58% than those of the conventional matching methods.

• 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.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.99-106
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• 2017

Electrical impedance tomography is a nondestructive imaging modality in which the internal resistivity distribution is reconstructed based on the injected currents and measured voltages inside a domain of interest. In this paper, an adaptive threshold value based region of interest (ROI) method is proposed to improve the spatial resolution of reconstructed images as well as to reduce the computational time of the inverse problem. Adaptive threshold value is calculated by INTERMODES method and ROI is determined from the domain based on this value. Moreover, the computational domain of image reconstruction is restricted within a ROI and iterative Gauss-Newton method is employed to estimate the resistivity distribution. To evaluate the performance of the proposed method, numerical experiments have been performed and the results are analyzed.

• Current Optics and Photonics
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• v.8 no.3
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• pp.215-224
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• 2024

In medium- and long-range optical imaging systems, atmospheric turbulence causes blurring and distortion of images, resulting in loss of image information. An image-restoration method based on an adaptive feature-fusion multi-input-multi-output (MIMO) dense U-shaped network (Unet) is proposed, to restore a single image degraded by atmospheric turbulence. The network's model is based on the MIMO-Unet framework and incorporates patch-embedding shallow-convolution modules. These modules help in extracting shallow features of images and facilitate the processing of the multi-input dense encoding modules that follow. The combination of these modules improves the model's ability to analyze and extract features effectively. An asymmetric feature-fusion module is utilized to combine encoded features at varying scales, facilitating the feature reconstruction of the subsequent multi-output decoding modules for restoration of turbulence-degraded images. Based on experimental results, the adaptive feature-fusion MIMO dense U-shaped network outperforms traditional restoration methods, CMFNet network models, and standard MIMO-Unet network models, in terms of image-quality restoration. It effectively minimizes geometric deformation and blurring of images.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.1
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• pp.1-10
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• 2004

Adaptive finite element analysis, in which its solution error meets with the user defined allowable error, is recently used to improve the reliability of finite element analysis results. This adaptive analysis is composed of two procedures; one is the error estimation of an analysis result and the other is the reconstruction of finite elements. In the (p-method, an element size is controlled by relocating of nodal positions (r-method) and the order of an element shape function is determined by the hierarchical polynomial (p-method) corresponding to the clement solution error by the enhanced SPR. In order to show the effectiveness and the accuracy of the suggested rp-method, various numerical examples were analyzed and these analysis results were examined by comparing with those obtained by the existed methods.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.345-350
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• 2000

Adaptive finite element analysis, which its solution error meets with the user defined allowable error, is recently used far improving reliability of finite element analysis results. This adaptive analysis is composed of two procedures; one is the error estimation of an analysis result and another is the reconstruction of finite elements. In the rp-method, an element size is controlled by relocating of nodal positions(r-method) and the order of an element shape function is determined by the hierarchical polynomial(p-method) corresponding to the element solution error. In order to show the effectiveness and accuracy of the suggested rp-method, various numerical examples were analyzed and these analysis results were examined by comparing with those obtained by the existed methods. As a result of this study, following conclusions are obtained. (1) rp-method is more accurate and effective than the r- and p-method. (2) The solution convergency of the rp-method is controlled by means of the iterative calculation numbers of the r- and p- method each other.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.1
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• pp.14-21
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• 2001

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. In indirect adaptive fuzzy control, based on the proved approximation capability of fuzzy systems, they are used to capture the unknown nonlinearities of the plant. Until now, most of the papers in the field of controller design for nonlinear system considers the affine system using fuzzy systems which have fixed grid-rule structure. We proposes a dynamic fuzzy rule insertion scheme where fuzzy rule-base grows as time goes on. With this method, the dynamic order of the controller reduces dramatically and an appropriate number of fuzzy rules are found on-line. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed-loop system is guaranteed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.252-255
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• 2020

Unsupervised deep learning methods have shown impressive results for the challenging monocular depth estimation task, a field of study that has gained attention in recent years. A common approach for this task is to train a deep convolutional neural network (DCNN) via an image synthesis sub-task, where additional views are utilized during training to minimize a photometric reconstruction error. Previous unsupervised depth estimation networks are trained within a fixed depth estimation range, irrespective of its possible range for a given image, leading to suboptimal estimates. To overcome this suboptimal limitation, we first propose an unsupervised adaptive depth estimation method guided by minimum and maximum (min-max) depth priors for a given input image. The incorporation of min-max depth priors can drastically reduce the depth estimation complexity and produce depth estimates with higher accuracy. Moreover, we propose a novel network architecture for adaptive depth estimation, called the AdaMM-DepthNet, which adopts the min-max depth estimation in its front side. Intensive experimental results demonstrate that the adaptive depth estimation can significantly boost up the accuracy with a fewer number of parameters over the conventional approaches with a fixed minimum and maximum depth range.

• Journal of Broadcast Engineering
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• v.8 no.1
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• pp.19-29
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• 2003

The demand for high-resolution images is gradually increasing, whereas many imaging systems yield aliased and undersampled images during image acquisition. In this paper, we propose a high-resolution image reconstruction algorithm considering inaccurate subpixel registration. A regularized Iterative reconstruction algorithm is adopted to overcome the ill-posedness problem resulting from inaccurate subpixel registration. In particular, we use multichannel image reconstruction algorithms suitable for application with multiframe environments. Since the registration error in each low-resolution has a different pattern, the regularization parameters are determined adaptively for each channel. We propose a methods for estimating the regularization parameter automatically. The preposed algorithm are robust against the registration error noise. and they do not require any prior information about the original image or the registration error process. Experimental results indicate that the proposed algorithms outperform conventional approaches in terms of both objective measurements and visual evaluation.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.219-226
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• 2017

Penalized-likelihood (PL) reconstruction methods for transmission tomography are known to provide improved image quality for reduced dose level by efficiently smoothing out noise while preserving edges. Unfortunately, however, most of the edge-preserving penalty functions used in conventional PL methods contain at least one free parameter which controls the shape of a non-quadratic penalty function to adjust the sensitivity of edge preservation. In this work, to avoid difficulties in finding a proper value of the free parameter involved in a non-quadratic penalty function, we propose a new adaptive method of space-variant smoothing with a simple quadratic penalty function. In this method, the smoothing parameter is adaptively selected for each pixel location at each iteration by using the image roughness measured by a pixel-wise standard deviation image calculated from the previous iteration. The experimental results demonstrate that our new method not only preserves edges, but also suppresses noise well in monotonic regions without requiring additional processes to select free parameters that may otherwise be included in a non-quadratic penalty function.