• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.3
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• pp.98-109
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• 2000

While the bispectrum has desirable properties in itself and therefore has a lot of potential to be applied to signal and Image restoration. few real-world application results have appeared in literature The major problem with this IS the difficulty In realizing the expectation operator of the true bispectrum, due to the lack of realizations. In this paper, the true bispectrum is defined as the expectation of the sample bispectrum, which IS the Fourier representation of the triple correlation given one realization The characteristics of the sample bispectrum are analyzed and a way to obtain an estimate of the true bispectrum without stochastic expectation, using the generalized theory of weighted regularization is shown. The bispectrum estimated by the proposed algorithm is experimentally demonstrated to be useful for signal recovery under blurred noisy condition.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.11
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• pp.116-122
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• 1998

We propose a spatially adaptive image interpolation algorithm, which can restore high frequency details in the original high resolution image. In order to apply the regularization approach to the interpolation procedure, we first present a two-dimensional separable image degradation model for a low resolution imaging system. According to the model, we propose a regularized spatially adaptive interpolation algorithm by using five different constraints. We also analyze convergence of the proposed algorithm, and provide some experimental results to compare the proposed algorithm with its nonadaptive version.

• Communications of the Korean Mathematical Society
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• v.16 no.3
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• pp.445-458
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• 2001

The shape and the distribution of material construction of the scatterer may be obtained from its scattered fields by the iterative inversion in the spectral domain. The illposedness, the resolution, and the uniqueness of the inversion are the key problems in the inversion and inter-related. The illposedness is shown to be caused by the evanescent modes which carries and amplifies exponentially the measurement errors in the back-propagation of the measured scattered fields. By filtering out all the evanescent modes in the cost functional defined as the squared difference between the measured and the calculated spatial spectrum of the scattered fields from the iteratively chosen medium parameters of the scatterer, one may regularize the illposedness of the inversion in the expense of the resolution. There exist many local minima of the cost functional for the inversion of the large and the high-contrast scatterer and the hybrid algorithm combining the genetic algorithm and the Levenberg-Marquardt algorithm is shown to find efficiently its global minimum. The resolution of reconstruction obtained by keeping all the propating modes and filtering out the evanescent modes for the regularization becomes 0.5 wavelength. The super resolution may be obtained by keeping the evanescent modes when the measurement error and instance, respectively, are small and near.

• Smart Structures and Systems
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• v.23 no.3
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• pp.295-306
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• 2019

Shanghai Tower is a 632-meter super high-rise building located in an area with wind and active earthquake. A sophisticated structural health monitoring (SHM) system consisting of more than 400 sensors has been built to carry out a long-term monitoring for its operational safety. In this paper, a reduced-order model including 31 elements was generated from a full model of this super tall building. An iterative regularized matrix method was proposed to tune the system parameters, making the dynamic characteristic of the reduced-order model be consistent with those in the full model. The updating reduced-order model can be regarded as a benchmark model for further analysis. A long-term monitoring for structural dynamic characteristics of Shanghai Tower under different construction stages was also investigated. The identified results, including natural frequency and damping ratio, were discussed. Based on the data collected from the SHM system, the dynamic characteristics of the whole structure was investigated. Compared with the result of the finite element model, a good agreement can be observed. The result provides a valuable reference for examining the evolution of future dynamic characteristics of this super tall building.

• 한국지구물리탐사학회:학술대회논문집
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• 2006.06a
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• pp.117-122
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• 2006

In the geophysical monitoring to understand the change of subsurface material properties with time, the time-invariant static subsurface model is commonly adopted to reconstruct a time-lapse image. This assumption of static model, however, can be invalid particularly when fluid migrates very quickly in highly permeable medium in the brine injection experiment. In such case, the resultant subsurface images may be severely distorted. In order to alleviate this problem, we develop a new least-squares inversion algorithm under the assumption that the subsurface model will change continuously in time. Instead of sampling a time-space model into numerous space models with a regular time interval, a few reference models in space domain at different times pre-selected are used to describe the subsurface structure continuously changing in time; the material property at a certain space coordinate are assumed to change linearly in time. Consequently, finding a space-time model can be simplified into obtaining several reference space models. In order to stabilize iterative inversion and to calculate meaningful subsurface images varying with time, the regularization along time axis is introduced assuming that the subsurface model will not change significantly during the data acquisition. The performance of the proposed algorithm is demonstrated by the numerical experiments using the synthetic data of crosshole dc resistivity tomography.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.619-628
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• 2009

LiDAR has capability of rapid data acquisition and provides useful information for reconstructing surface of the Earth. However, Extracting information from LiDAR data is not easy task because LiDAR data consist of irregularly distributed point clouds of 3D coordinates and lack of semantic and visual information. This thesis proposed methods for automatic extraction of buildings and 3D detail modeling using airborne LiDAR data. As for preprocessing, noise and unnecessary data were removed by iterative surface fitting and then classification of ground and non-ground data was performed by analyzing histogram. Footprints of the buildings were extracted by tracing points on the building boundaries. The refined footprints were obtained by regularization based on the building hypothesis. The accuracy of building footprints were evaluated by comparing with 1:1,000 digital vector maps. The horizontal RMSE was 0.56m for test areas. Finally, a method of 3D modeling of roof superstructure was developed. Statistical and geometric information of the LiDAR data on building roof were analyzed to segment data and to determine roof shape. The superstructures on the roof were modeled by 3D analytical functions that were derived by least square method. The accuracy of the 3D modeling was estimated using simulation data. The RMSEs were 0.91m, 1.43m, 1.85m and 1.97m for flat, sloped, arch and dome shapes, respectively. The methods developed in study show that the automation of 3D building modeling process was effectively performed.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.4 s.27
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• pp.21-27
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• 2003

The rational polynomial coefficients(RPC) model is a generalized sensor model that is used as an alternative for the physical sensor model for IKONOS-2 and QuickBird. As the number of sensors increases along with greater complexity, and as the need for standard sensor model has become important, the applicability of the RPC model is also increasing. The RPC model can be substituted for all sensor models, such as the projective camera the linear pushbroom sensor and the SAR This paper is aimed at generating a RPC model from the physical sensor model of the KOMPSAT-1(Korean Multi-Purpose Satellite) and aerial photography. The KOMPSAT-1 collects $510{\sim}730nm$ panchromatic images with a ground sample distance (GSD) of 6.6m and a swath width of 17 km by pushbroom scanning. We generated the RPC from a physical sensor model of KOMPSAT-1 and aerial photography. The iterative least square solution based on Levenberg-Marquardt algorithm is used to estimate the RPC. In addition, data normalization and regularization are applied to improve the accuracy and minimize noise. And the accuracy of the test was evaluated based on the 2-D image coordinates. From this test, we were able to find that the RPC model is suitable for both KOMPSAT-1 and aerial photography.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.64-74
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• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.