• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.293-296
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• 2003

The enhancement of fingerprint image is necessary to improve the performance of fingerprint recognition. The enhancement of fingerprint image with Gabor Filter(GF) is widely used. However GF has the weakness such as long processing time and the sensitivity to ridge frequency. To overcome these weaknesses, we propose a Modified Anisotropic Gaussian Filter(MAGF) which is modified from Anisotropic Filter proposed by S. Greenburg's(SAF). This proposed MAGF can reduce the calculation time of ridge frequency and improve the weakness of sensitivity to ridge frequency. We also explained that MAGF is better than others mathematically and experimentally.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.4
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• pp.325-334
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• 2001

It is shown that the multiscale prewhitening matched filter for detecting Gaussian objects in Markov noise can be implemented by the undecimated wavelet transform with a biorthogonal spline wavelet. If the object to be detected is Gaussian shaped and its scale coincides with one of those computed by the wavelet transform, and if the background noise is truly Markov, then optimum detection is realized by thresholding the appropriate details image. Our detection algorithm is applied to the digitized mammograms for detecting microcalcifications. However, microcalcifications are not exactly Gaussian shaped and its background noise may not be Markov. In order to campensate for these discrepancy, Hotelling observer is employed, which is applied to feature vectors comprised of 3-octave wavelet coefficients.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.471-478
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• 2007

The SA fillers encompass a large class of filters based on order statistics as veil as linear FIR filters. Using SA later structure, it is possible to design linear and non-linear filters under a unified framework. In this paper SA filters are applied to an image smoothing problem for mixed noise. Original image is contaminated by Gaussian and impulsive noise. Optimal SA filters are designed and applied to contaminated image. The experimental result shows that SA filters outperform linear FIR and ordering-based nonlinear filters.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.4
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• pp.61-70
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• 2012

We propose a method that finds candidate targets based on morphology and detects a small target from them using modified gaussian distance function. The existing small target detection methods use predictive filters or morphology. The methods using predictive filters take long to approach least errors. The methods using morphology are weak at clutters and need to consider size of a small target when selecting size of structure elements. We propose a robust method for small target detection to complete the existing methods. First, the proposed method deletes clutters using a median filter. Next, it does closing and opening operation using various size of structure elements, and figures target candidate pixels with subtraction operation between the results of closing and opening operation. It detects an exact small target using a gaussian distance function from the candidates target areas. The proposed method is less sensitive to clutters, and shows a detection rate of 98%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2447-2453
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• 2013

The digital images are being degraded by noise in the process of acquisition, storage and transmission, Gaussian or impulse noise is the representative noise. Meanwhile, the image has lots of tendency to be degraded by complex noise, so various researches are being conducted for reducing these complex noise. In this paper, to remove complex noise, the algorithm processed by modified switching median filter and modified adaptive weighted filter according to the result after judging the kinds of noise is proposed. In the simulation result, excellent denoising capabilities. Furthermore, we compared proposed algorithm with existing methods for objective judgement, and PSNR(peak signal to noise ratio) is used by the criterion of judgement.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.253-260
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• 2008

Image denoising as one of image enhancement methods has been studied a lot in the spatial and transform domain filtering. Recently wavelet transform which has an excellent energy compaction and a property of multiresolution has widely used for image denoising. But a transform based on human visual system is visually useful if an end user is human beings. Therefore, Gabor cosine and sine transform which is considered as human visual filter is applied to image denoising areas in this paper. Denoising performance of the proposed transform is compared with those of the derivatives of Gaussian transform being another human visual filter and of discrete wavelet transform in terms of PSNR. With three levels of various noises, experimental results for real images show that the proposed transform has better PSNR performance of 0.41dB than DWT and 0.14dB than DGT.

• Journal of IKEEE
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• v.22 no.3
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• pp.810-815
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• 2018

The kernelized correlation filter algorithm yielded meaningful results in accuracy for object tracking. However, because of the use of a fixed size template, we could not cope with the scale change of the tracking object. In this paper, we propose a method to track objects by finding the best scale for each frame using correlation filtering response values in multi-scale using nearest neighbor interpolation and Gaussian normalization. The scale values of the next frame are updated using the optimal scale value of the previous frame and the optimal scale value of the next frame is found again. For the accuracy comparison, the validity of the proposed method is verified by using the VOT2014 data used in the existing kernelized correlation filter algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.1064-1070
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• 2012

Nowadays, image processing has been applied in a variety of fields. In order to preserve the high quality of visual the degradation phenomenon for images should be removed. Noise is one of the representative elements cause of the degradation phenomenon and AWGN(additive white Gaussian noise) always damages images. In this paper, an mixed filter algorithm, which is based on parallel denoising method, is proposed to suppress AWGN. This algorithm parallels the spatial domain wiener filter and the wavelet domain thresholding method which thresholding function is selected based on scale level. The proposed modified thresholding function which considers the dependency between parent and child coefficient performs well on suppressing noise.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1227-1232
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• 2013

Recently, the expectation of quality about images over the increasing demand of digital devices is increasing with the development of the technology of the digital. But the images are degraded by a variety of causes, and the main reason is the noises. Therefore, the necessity of denoising comes to the fore, and the research for denoising is progressing dynamically. The images are mainly degraded by AWGN(additive white Gaussian noise), and the characteristics of denoising of existing methods such as mean filter are insufficient. In this paper, an algorithm combined by the spatial weighted filter and the modified adaptive weighted filter is proposed in order to effectively remove the AWGN. In the simulation result, the proposed algorithm showed excellent denoising capabilities.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.104-107
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• 2018

최근 촬영 기기의 기술발전으로 인해 디지털 영상의 해상도가 증가함에 따라 선명한 디지털 영상에 대한 요구가 증가하고 있다. 이러한 요구에도 불구하고 디지털 영상 내 가우시안 잡음 (gaussian noise)은 촬영기기를 통해 영상 획득 및 처리 과정에서 발생하여 화질을 열화 시킨다. 디지털 이미지에서 발생하는 가우시안 잡음을 제거하기 위해서 기존의 저대역 통과 필터 (low-pass filter: LPF)를 사용하면 잡음은 제거되지만, 블러링 현상 (blurring phenomenon)이 나타난다. 이러한 문제점을 개선하기 위해 소벨 연산자 (sobel operator)를 사용하여 영상 내 에지 맵 (edge-map)을 생성하여 에지 영역과 동질 영역을 구분한다. 에지영역에서는 약한 저역 필터 (weak low-pass filter)를 사용하고, 그 외의 이미지 영역에서는 강한 저역 필터 (strong low-pass filter)를 사용하는 알고리듬을 제안하였다. 그리고 다양한 이미지에 대하여 기존 알고리듬과 제안한 알고리듬의 적용한 결과를 통해 주관적 화질 비교하였고 객관적 지표로 최대 신호 대 잡음비 (peak signal-to noise ratio: PSNR)와 구조 유사성 (structural similarity: SSIM)을 사용하여 성능을 평가하였다. 실험결과를 통해 제안된 알고리듬이 잡음 제거 및 외곽선 보존의 우수함을 확인하였다.