• The KIPS Transactions:PartA
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• v.9A no.2
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• pp.259-266
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• 2002

In this paper we implemented various image processing filtering using the format converter. This design method is based on realized the large processor-per-pixel array by integrated circuit technology. These two types of integrated structure are can be classify associative parallel processor and parallel process DRAM (or SRAM) cell. Layout pitch of one-bit-wide logic is Identical memory cell pitch to array high density PEs in integrate structure. This format converter design has control path implementation efficiently, and can be utilize the high technology without complicated controller hardware. Sequence of array instruction are generated by host computer before process start, and instructions are saved on unit controller. Host computer is executed the pixel-parallel operation starting at saved instructions after processing start. As a result, we obtained three result that 1) simple smoothing suppresses higher spatial frequencies, reducing noise but also blurring edges, 2) a smoothing and segmentation process reduces noise while preserving sharp edges, and 3) median filtering may be applied to reduce image noise. Median filtering eliminates spikes while maintaining sharp edges and preserving monotonic variations in pixel values.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.552-562
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• 2018

With the advent of the information society, image restoration technology has aroused considerable interest. Guided image filtering is more effective in suppressing noise in homogeneous regions, but its edge-preserving property is poor. As such, the critical part of guided filtering lies in the selection of the guided image. The result of the Expected Patch Log Likelihood (EPLL) method maintains a good structure, but it is easy to produce the ladder effect in homogeneous areas. According to the complementarity of EPLL with guided filtering, we propose a method of coupling EPLL and guided filtering for image de-noising. The EPLL model is adopted to construct the guided image for the guided filtering, which can provide better structural information for the guided filtering. Meanwhile, with the secondary smoothing of guided image filtering in image homogenization areas, we can improve the noise suppression effect in those areas while reducing the ladder effect brought about by the EPLL. The experimental results show that it not only retains the excellent performance of EPLL, but also produces better visual effects and a higher peak signal-to-noise ratio by adopting the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.55-62
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• 2007

This paper proposes method to get exquisite third dimension data removing included noise by error that occur in third dimension reconstruction through camera auto-calibration. Though reconstructing third dimension data by previous noise removing method, mesh that area is wide is happened problem by noise. Because mesh's area is important, the proposed algorithm need preprocessing that remove unnecessary triangle meshes of acquired third dimension data. The research analyzes the characteristics of noise using the area information of 3-dimensional meshes, separates a peek noise and a Gauss noise by its characteristics and removes the noise effectively. We give a quantitative evaluation of the proposed preprocessing filter and compare with the mesh smoothing procedures. We demonstrate that our effective preprocessing filter outperform the mesh smoothing procedures in terms of accuracy and resistance to over-smoothing.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.305-307
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• 2016

The purpose of this paper is to determine the optimal values of the gain parameters used in the tracking module for a highly dynamic warship. The algorithm of the tracking module uses the ${\alpha}-{\beta}-{\gamma}$ filter to compute accurate estimates and update the state variables, that is, positions, velocity and acceleration. The filtering coefficients ${\alpha}$, ${\beta}$ and ${\gamma}$ are determined from set values of the damping parameter, ${\xi}$. Optimization is achieved by plotting a range of the damping parameter ${\xi}$ against the corresponding residual error and then selecting the best value of ${\xi}$ with the minimum residual error. Optimal values of the smoothing coefficients are subsequently computed from the selected damping parameter, ${\xi}$.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.3
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• pp.289-297
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• 2011

The explicit finite element method is an essential tool for solving large problems with severe nonlinear characteristics, but its results can be difficult to interpret. In particular, it can be impossible to evaluate its acceleration responses because of severe discontinuity, extreme noise or aliasing. We suggest a new post-processing method for transient responses and their response spectra. We propose smoothing methods using a Gaussian kernel without in depth knowledge of the complex frequency characteristics; such methods are successfully used in the filtering of digital signals. This smoothing can be done by measuring the velocity results and monitoring the response spectra. Gaussian kernel smoothing gives a better smoothness and representation of the peak values than other approaches do. The floor response spectra can be derived using smoothed accelerations for the design.

• Journal of Biomedical Engineering Research
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• v.22 no.4
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• pp.311-319
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• 2001

Statistical reconstruction methods in the context of a Bayesian framework have played an important role in emission tomography since they allow to incorporate a priori information into the reconstruction algorithm. Given the ill-posed nature of tomographic inversion and the poor quality of projection data, the Bayesian approach uses regularizers to stabilize solutions by incorporating suitable prior models. In this work we show that, while the quantitative performance of the standard filtered backprojection (FBP) algorithm is not as good as that of Bayesian methods, the application of spline-regularized smoothing to the sinogram space can make the FBP algorithm improve its performance by inheriting the advantages of using the spline priors in Bayesian methods. We first show how to implement the spline-regularized smoothing filter by deriving mathematical relationship between the regularization and the lowpass filtering. We then compare quantitative performance of our new FBP algorithms using the quantitation of bias/variance and the total squared error (TSE) measured over noise trials. Our numerical results show that the second-order spline filter applied to FBP yields the best results in terms of TSE among the three different spline orders considered in our experiments.

• International Journal of Aeronautical and Space Sciences
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• v.3 no.2
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• pp.88-94
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• 2002

This paper describes attitude determination algorithm for the low earth orbit(LEO) spacecraft using stellar inertial sensors. The cascaded gyro/star tracker extended Kalman filter is constructed to fuse two sensor data. And then the smoothing of the measurement are proposed for an unreasonable jump of star tracker. The smoothing algorithm for the rejection of star tracker error jumps is designed by scalar adaptive filter. The proposed algorithms operate to process the measurement of gyro/star tracker Kalman filter, therefore, it is comparatively simple to apply these methods to other integration systems. Simulations to gyro/star tracker integrated system show that the proposed method is effective.

• Journal of Biomedical Engineering Research
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• v.19 no.6
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• pp.551-562
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• 1998

Amultiresolution filtering algorithm based on the physical SPECT lesion detachability provides and optimal solution for SPECT reconstruction problem. Related to the previous study, we estimated the SPECT lesion detection capability by m minimum detectable lesion sizes (MDLSs), and generated m reconstruction filters which are designed to maximize the smoothing effect at a fixed MDLS-dependent resolution level $\frac{MDLS}{4\sqrt{2In2}}$. The proposed multiresolution filtering algorithm used a coarse-to-fine approach for the m-level resolution filter images obtained from these m filters for a given projection image. First, the local homogeneity is determined for every pixel of the filter images, by comparing the local variance value computed in a window centered at the pixel and the mode determined from the distribution of the local variances. Based on the local homogeneity, the pixels declared as homogeneous are chosen from the filter image of the lowest resolution, and for the other pixels the same process is repeated for the higher resolution filter images. For the non-homogeneous pixels after this pixels after this repetition process ends, the pixel values of the highest resolution filter image are substituted. From the results of the simulated experiments, the proposed multiresolution filtering algorithm showed a strong smoothing effect in the homogeneous regions and a significant resolution improvement near the edge regions of the projection images, and so produced good adaptability effects in the reconstructed images.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.243-255
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• 1997

Forecasting the future values of an observed time series is an important problem in many areas, including economics, traffic engineering, production planning, sales forecasting, and stock control. The purpose of this paper is aimed to discover the more efficient forecasting model through the parameter estimation and residual analysis among the quantitative method such as Winters' exponential smoothing model, Box-Jenkins' model, and Kalman filtering model. The mean of the time series is assumed to be a linear combination of known functions. For a parameter estimation and residual analysis, Winters', Box-Jenkins' model use Statgrap and Timeslab software, and Kalman filtering utilizes Fortran language. Therefore, this paper can be used in real fields to obtain the most effective forecasting model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.1021-1027
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• 2000

In this paper, we propose two new detection-estimation based algorithms that effectively remove impulsive noises in severely corrupted color images. The existing methods for enhancing corrupted color images with impulsive noises commonly possess the inherent problems of excessive computing time and smoothing out edges. However, since our proposed algorithms classify corrupted pixels first in each channel or in each pixel and then perform marginal or vector median filtering only for them, are computationally efficient and preserve edges well. In addition, since there are no appropriate criteria to evaluate the performance of impulsive noise detectors for color images, the objective comparison of noise detectors is difficult. Thus, we introduce a new efficiency factor to compare the performance of noise detectors in digital color images. Simulation results show that the proposed algorithms perform better than the existing methods in terms of both objective and subjective evaluat ons.