• Spatial Information Research
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• v.2 no.2
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• pp.189-196
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• 1994

Adaptive smoothing filter is a filter that averages out the intensities around the pixels of similar intensities while conserving the discontinuties. When human eyes rec-ognize a map, the brain can easily assign one color for each element such as road or building while computer distinguishes all the minute color differences even for one ele¬ment. We can approach to the solution by using the adaptive smoothing filter so that the machine can assign one color for each element as much as we want, and it is found to be a very essential tool foor map segmentation of urban areas. The filter is applied to a scanned map, and it is used to extract roads and residential areas.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.5
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• pp.462-471
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• 1989

Two-dimensional median filters were affectively supprssing the noise in image processing with the adge smearing decreased. However, if the window were large as necessary in noise then the filter had tendency to cut off corners. An estimate of gradient was used to decide how the ouputs of the filters were calculated. For parallel to the gradient direction we used edge preserving median operation and orthogonal to that averaging subfilters over which medians were then chosen. Four different algorithms were introduced.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.198-200
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• 2019

배터리의 상태를 추정하기 위해 전압과 전류 데이터는 사용자가 센서를 통해 얻을 수 있는 정보이며, 이때 노이즈 성분이 포함된 전압 및 전류 데이터는 배터리의 상태 추정을 할 때 정확도를 크게 감소시킬 수 있다. 기존의 확장 칼만필터(EKF, Extended Kalman Filter)를 사용하여 노이즈 성분이 포함된 데이터를 통해 배터리의 상태를 추정했을 때는 노이즈의 영향으로 인해 추정 정확도가 떨어진다. 본 논문은 적응형 칼만 필터(AKF, Adaptive Kalman Filter)를 사용하여 노이즈 분산값을 업데이트 해줌으로써 SOC추정 성능을 향상시켰다. 실험 및 배터리의 모델링은 21700 NMC 고용량 배터리를 사용하였으며, 배터리의 전압에 임의의 노이즈 성분을 추가하여 배터리의 SOC를 추정 정확도를 검증 하였다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.9 no.4
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• pp.11-22
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• 1999

Least-mean-square(LMS) adaptive filters have proven to be extremely useful in a number of signal processing tasks. However LMS adaptive filter suffer from a slow rate of convergence for a given steady-state mean square error as compared to the behavior of recursive least squares adaptive filter. In this paper an efficient signal interference control technique is introduced to improve the convergence speed of LMS algorithm with tap weighted vectors updating which were controled by reusing data which was abandoned data in the Adaptive transversal filter in the scheme with data recycling buffers. The computer simulation show that the character of convergence and the value of MSE of proposed algorithm are faster and lower than the existing LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer algorithm could efficiently reject ISI of channel and increase speed of convergence in avoidance burden of computational complexity in reality when it was experimented having the same condition of LMS algorithm.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.141-147
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• 2017

Scalable extension of High Efficiency Video Coding (SHVC) standard uses the up-sampled residual data from the base layer to make a residual data in the enhancement layer. This paper describes an efficient algorithm for improving coding gain by using the filtered residual signal of base layer in the Scalable extension of High Efficiency Video Coding (SHVC). The proposed adaptive filter selection mechanism uses the smoothing and sharpening filters to enhance the quality of inter-layer prediction. Based on two filters and the existing up-sampling filter, a rate-distortion (RD)-cost fuction-based competitive scheme is proposed to get better quality of video. Experimental results showed that average BD-rate gains of 1.5%, 2.1%, and 1.7% for Y, U and V components, respectively, were achieved, compared with SHVC reference software 5.0, which is based on HEVC reference model (HM) 13.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.124-132
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• 2014

In this paper, we present a novel backlit region detection and contrast enhancement method using fuzzy C-means clustering and adaptively partitioned block based contrast stretching. The proposed method separates an image into both dark backlit and bright background regions using adaptively partitioned blocks based on the optimal threshold value computed by fuzzy logic. The detected block-wise backlit region is refined using the guided filter for removing block artifacts. Contrast stretching algorithm is then applied to adaptively enhance the detected backlit region. Experimental results show that the proposed method can successfully detect the backlit region without a complicated segmentation algorithm and enhance the object information in the backlit region.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.623-625
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• 2012

The restoration of an image corrupted by Gaussian noise is an important task in image processing. There are many kinds of filters are proposed to remove Gaussian noise such as Gaussian filter, mean filter, weighted filter, etc. However, they perform not good enough for denoising and edge preservation. Hence, in this paper we proposed an adaptive weighted filter which considers spatial distance and the estimated variance of noise. We also compared the proposed method with existing methods through the simulation and used MSE(mean squared error) as the standard of judgement of improvement effect.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.883-885
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• 2013

Image restoration refers to removing different kinds of noise added to image, and to reducing effect of noise upon image. For image restoration, some methods such as mean filter, median filter and weighted filter were proposed, but the existing methods have poor denoising and edge-reserved performance. Therefore, in this paper modified median filter algorithm was proposed that enlarges mask size according to median value of mask in order to remove noise efficiently. And, it was compared by simulation to the existing methods, and MSE(mean squared error) was used on a criterion of evaluation.

• Journal of Broadcast Engineering
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• v.16 no.1
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• pp.1-13
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• 2011

In this paper, we propose an adaptive in-loop filter to improve the coding efficiency. Recently, there are post-filter hint SEI and block-based adaptive filter control (BAFC) methods based on the Wiener filter which can minimize the mean square error between the input image and the decoded image in video coding standards. However, since the post-filter hint SEI is applied only to the output image, it cannot reduce the prediction errors of the subsequent frames. Because BAFC is also conducted with a deblocking filter, independently, it has a problem of high computational complexity on the encoder and decoder sides. In this paper, we propose the low-complexity adaptive in-loop filter (LCALF) which has lower computational complexity by using H.264/AVC deblocking filter, adaptively, as well as shows better performance than the conventional method. In the experimental results, the computational complexity of the proposed method is reduced about 22% than the conventional method. Furthermore, the coding efficiency of the proposed method is about 1% better than the BAFC.

• KIISE Transactions on Computing Practices
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• v.20 no.11
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• pp.573-578
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• 2014

In this letter, we propose a noise reduction method for use in magnetic resonance images that is based on non-local mean and guided image filters. Our method consists of two phases. In the first phase, the guidance image is obtained from a noisy image by using an adaptive non-local mean filter. The spread of the kernel is adaptively by controlled by implementing the concept of edgeness. In the second phase, the noisy images and the guidance images are provided to the guided image filter as input in order to produce a noise-free image. The improved performance of the proposed method is investigated by conducting experiments on standard datasets that contain magnetic resonance images. The results show that the proposed scheme is superior over the existing approaches.