• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2006.06a
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• pp.53-56
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• 2006

In this paper, we present the algorithm to enhance the high frequency components of the image by subtracting smoothing version of an image from the original image. In a word, this is the technique to get more a precise and vivid image. The method of Gaussian smoothing is accepted to get the components of the flat image. Moreover we need to consider the size of the gain of the proposed filter in order to preserve the overall brightness of a image. Based on the algorithm verified by MATLAB, we can obtain more vivid and fuller detail of the image than an original image.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.69-77
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• 2017

This paper represents a novel framework for multi-scale image fusion. Multi-scale Kalman Smoothing (MKS) algorithm with quad-tree structure can provide a powerful multi-resolution image fusion scheme by employing Markov property. In general, such approach provides outstanding image fusion performance in terms of accuracy and efficiency, however, quad-tree based method is often limited to be applied in certain applications due to its stair-like covariance structure, resulting in unrealistic blocky artifacts at the fusion result where finest scale data are void or missed. To mitigate this structural artifact, in this paper, a new scheme of multi-scale fusion framework is proposed. By employing Super Resolution (SR) technique on MKS algorithm, fine resolved measurement is generated and blended through the tree structure such that missed detail information at data missing region in fine scale image is properly inferred and the blocky artifact can be successfully suppressed at fusion result. Simulation results show that the proposed method provides significantly improved fusion results in the senses of both Root Mean Square Error (RMSE) performance and visual improvement over conventional MKS algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.13-20
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• 2005

In this paper, we propose new smoothing filters, i.e., occluding patterns that can accurately correct disparities of occluded areas in the estimated disparity map. An image is composed of several layers and each layer presents similar disparity. Furthermore, the distribution of the estimated disparities has a specific direction around the boundary of the occlusion, and this distribution presents the different direction with respect to the left- and the right-based disparity map. However, typical smoothing filters, such as mean filter and median filter, did not take into account those characteristic. So, they can decrease some error, but they cannot guarantee the accuracy of the corrected disparity. On the contrary, occluding patterns can accurately correct disparities of occluded areas because they consider both the characteristic that occlusion occurs and the characteristic that disparities of the occlusion are ranged, from estimated disparity maps with respect to the left and the right images. We made experiments on occluding patterns with some real stereo image set, and as a result, we can correct disparities of occluded areas more accurately than typical smoothing filters did.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.237-238
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• 2013

The quality of document image captured from electronic display might be worse when it is compared with document image captured from paper. The problem appears because of Moir? noise. This problem can lead to achieve inaccurate intermediate result for further image processing. This paper proposes a method to remove Moir? noise of document images captured from electronic display. The proposed algorithm is separated in two parts. In the first step, it corrects the text area region (foreground) with small area of smoothing. Then, it corrects the background area with large area of smoothing.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.4
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• pp.24-29
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• 1999

In this paper, a simple contour-preserving filtering algorithm is proposed. The goal of the contour-preserving filtering method is to remove noise ad granularity as the preprocessing for the image segmentation procedure. Our method finds edge map and separates the image into the edge region and the non-edge region using this edge map. For the non-edge region, typical smoothing filters could be used to remove the noise and the small areas during the segmentation procedure. The result of simulation shows that our method is slightly better than the typical methods such as the median filtering and gradient inverse weighted filtering in the point of view of analysis of variance (ANOVA).

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.28 no.1
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• pp.73-85
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• 1998

The purpose of this study was to aid in the use of the digital images by edge-detect interpolation for direct digital periapical images using edge-detect interpolation. This study was performed by image processing of 20 digital periapical images; pixel replication, linear non-interpolation, linear interpolation. and edge-sensitive interpolation. The obtained results were as follows : 1. Pixel replication showed blocking artifact and serious image distortion. 2. Linear interpolation showed smoothing effect on the edge. 3. Edge-sensitive interpolation overcame the smoothing effect on the edge and showed better image.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3694-3702
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• 1999

OSL algorithm has an advantage that repeated algorithm is easily derived, even though penalty function which has a complicated transcendental function. In order to solve this problem, we suggested MPEMG algorithm. However, though this algorithm extend convergence rate of smoothing constant, it include the problem that is not faster than OSL algorithm in the convergence rate increasing penalized log-likelihood. Accordingly, in this paper, we will suggest SMOSLG digital image restoration algorithm which is fast in the convergence rate as well as extend convergence region of smoothing constant. And also we will study the usefulness of algorithm suggested through digital image simulation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06a
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• pp.33-36
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• 1998

Most image restoration problems are ill-posed and need to e regularized. A difficult task in image regularization is to avoid smoothing of image edges. In this paper, were proposed an edge-preserving image restoration algorithm using block-based edge classification. In order to exploit the local image characteristics, we classify image blocks into edge and no-edge blocks. We then apply an adaptive constrained least squares (CLS) algorithm to eliminate noise around the edges. Experimental results demonstrate that the proposed algorithm can preserve image edges during the regularization process.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1988.10a
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• pp.262-265
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• 1988

• Geophysics and Geophysical Exploration
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• v.12 no.4
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• pp.347-354
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• 2009

Physical properties of rocks are strongly dependant on details of pore micro-structures, which can be used for quantifying relations between physical properties of rocks through pore-scale simulation techniques. Recently, high-resolution scan techniques, such as X-ray microtomography and high performance computers make it possible to calculate permeability from pore micro-structures of rocks. We try to extend this simulation methodology to velocity and electrical conductivity. However, the smoothing effect during tomographic inversion creates artifacts in pore micro-structures and causes inaccurate property estimation. To mitigate this artifact, we tried to use sharpening filter and neural network classification techniques. Both methods gave noticeable improvement in pore structure imaging and accurate estimation of permeability and electrical conductivity, which implies that our method effectively removes the smoothing effect in pore structures. However, the calculated velocities showed only incremental improvement. By comparison between thin section images and tomogram, we found that our resolution is not high enough, and it is mainly responsible for the inaccuracy in velocity despite the successful removal of the smoothing effect. In conclusion, our methods can be very useful for pore-scale modeling, since it can create accurate pore structure without the smoothing effect. For accurate velocity estimation, the resolution of pore structure should be at least three times higher than that for permeability simulation.