• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.422-423
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• 2018

In this paper, we implement Frenelet transform for decomposition of the fringe pattern and analyze its characteristics. The implemented wavelet-like basis functions are well suited for reconstruction and processing of optically generated Fresnel holograms. After analyzing the characteristics of the B-spline function, we will discuss the wavelet-like multi-resolution analysis method. Through this process, we implemented a transform tool that can decompose fringe patterns effectively. We have implemented a B-spline function with various decomposition properties and showed the results of decomposing the fringe pattern.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3C
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• pp.387-397
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• 2004

The wavelet transform providing both of the frequency and spatial information of an image is proved to be very much effective for the compression of images, and recently lot of studies on coding algorithms for images decomposed by the wavelet transform together with the multi-resolution theory are going on. This paper proposes a quadtree decomposition method of image compression applied to the images decomposed by wavelet transform by using the correlations between pixels and '0'data grouping. Since the coefficients obtained by the wavelet transform have high correlations between scales and high concentrations, the quadtree method can reduce the data quantity effectively. the experimental infrared image with 256${\times}$256 size and 8〔bit〕, was used to compare the performances of the existing and the proposed compression methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.397-404
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• 2002

In this paper, it is proposed wavelet neural network using the fuzzy concept with the fuzzy and the multi-resolution analysis(MRA) of wavelet transform. Also, it wishes to improve any nonlinear function learning approximation using this system. Here, the fuzzy concept is used the bell type fuzzy membership function. And the composition of wavelet has a unit size. It is used the backpropagation algorithm for learning of wavelet neural network using the fuzzy concept. It is used the multi-resolution analysis of wavelet transform, the bell type fuzzy membership function and the backpropagation algorithm for learning. This structure is confirmed to be improved approximation performance than the conventional algorithms from one dimension and two dimensions function through simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.930-938
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• 2005

We propose a content-based image retrieval(CBIR) method based on an efncient combination of a color feature and multiresolution texture features. As a color feature, a HSV autocorrelograrn is chosen which is blown to measure spatial correlation of colors well. As texture features, BDIP and BVLC moments are chosen which is hewn to measure local intensity variations well and measure local texture smoothness well, respectively. The texture features are obtained in a wavelet pyramid of the luminance component of a color image. The extracted features are combined for efficient similarity computation by the normalization depending on their dimensions and standard deviation vectors. Experimental results show that the proposed method yielded average $8\%\;and\;11\%$ better performance in precision vs. recall than the method using BDIPBVLC moments and the method using color autocorrelograrn, respectively and yielded at least $10\%$ better performance than the methods using wavelet moments, CSD, color histogram. Specially, the proposed method shows an excellent performance over the other methods in image DBs contained images of various resolutions.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.152-153
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• 2014

본 논문에서는 이차전지의 특성비교/분석을 위해 이산 웨이블릿 변환(DWT;discrete wavelet transform)과 웨이블릿 패킷 변환(WPT;wavelet packet transform)을 적용한 연구를 소개한다. 다해상도 분석(MRA; multi resolution analysis)의 시간-주파수 분석을 통해 저주파 성분(approximation;$A_n$)과 고주파 성분(detail;$D_n$)로 분해되는 것은 두 방법 동일하다. 하지만, 이산 웨이블릿 변환이 단순히 저대역 부분만 계속 분해하는 것과 달리 웨이블릿 패킷 변환은 저대역과 고대역을 모두 분해하여 높은 분해성능을 가지는 웨이블릿의 일반화이다. 웨이블릿 패킷 변환을 자세히 소개하고 이를 이차전지에 적용하여 이산 웨이블릿 변환과의 상관성을 정리하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.285-288
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• 2013

의료영상에서의 노이즈는 환자 진단에 있어서 막대한 영향을 미치는 영상의 화질을 떨어트림으로써, 진단에 대한 유효성을 낮추게 된다. 특히, 현재 이슈화 되고 있는 저선량 의료영상은 기존의 고선량 의료영상보다 노이즈 레벨이 높으며, 이에 따라서 의료영상에서의 노이즈 제거 기술은 매우 중요한 사안으로 부각되고 있다. 본 논문에서 제시하는 노이즈 제거 기술은 각각의 투영 영상을 여러개의 부대역(sub-band)으로 분해하는 것으로부터 시작한다. 분해된 각각의 부대역 영상은 엣지 검출기를 통하여 엣지 부분과 평탄한 영역으로 구별되어 진다. 검출된 엣지는 0 ~ 1 사이의 값으로 정규화 되며, 퍼지기반의 연산을 통하여 엣지의 확실성을 나타내는 엣지맵으로 변환하게 된다. 이 엣지맵을 통하여 각 부대역 영상의 필터링 정도를 제어하고, 분해된 각 부대역을 결합하는 방식을 취함으로써 영상의 엣지 부분을 최대한 보존하면서 노이즈는 효과적으로 제거하도록 하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.372-378
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• 2014

Noise in biomedical X-ray image degrades the quality so that it might causes to decrease the accuracy of diagnosis. Especially the noise reduction techniques is quite essential for low-dose biomedical X-ray images obtained from low radiation power in order to protect patients, because their noise level is usually high to well discriminate objects. This paper proposes an efficient method to remove the noise in low-dose X-ray images while preserving the edges with diverse resolutions. In the proposed method, a noisy image is at first decomposed into several images with different resolutions in pyramidal representation, then the stable map of edge confidence is obtained from each of analyzed image using a fuzzy logic-based edge detector. This map is used to adaptively determine the parameter for guided filters, which eliminate the noise while preserving edges in the corresponding image. The filtered images in the pyramid are extended and synthesized into a resulted image using interpolation technique. The superiority of proposed method compared to the median, bilateral, and guided filters has been experimentally shown in terms of noise removal and edge preserving properties.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06e
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• pp.181-184
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• 1998

웨이브렛 변환은 신호나 영상을 분석하기 위한 다해상도 분해기법으로 사용되어 왔다. 웨이브렛 변환영역에서 신호는 스케일과 위치상의 크기로 표현된다. 이 변환영역에서는 신호나 영상의 주파수 성분들이 각각의 스케일에 따라서 분리되어 나타난다. 또한 각 변환영역은 신호나 영상의 공간적인 특성을 상당부분 포함하고 있다. 이러한 웨이브렛 변환의 특성은 푸리에 변화에 기초한 방법과는 달리, 에지와 잡음성분을 효과적으로 분리할 수 있는 정보를 우리에게 제공해 준다. 본 논문에서는 웨이브렛 변환영역의 각 스케일 특성과 공간적인 특성을 이용하여 영상의 잡음성분을 제거하였다. 잡음제거 기법의 성능평가를 위해 Wiener 필터링 방법과 비교하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.72-79
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• 2007

In this paper, It propose a new restoration algorithm of high resolution, which is reconstructed to high resolution image using low resolution image informations. The proposed algorithm is constructed based on super resolution theory, it is consisted of progressive steps of the integration and construction. It reduced a lot of data-processing capacity and noise with integration through sub-pixel movement and wavelet basis through a higher resolution. As a result, it is shown that the main information is maintained and the error rate is improved. Using expansion fuzzy wavelet B-spline interpolation in stage of construction, it is confirmed that we can achieve smoothing image and good resolution without blur and block.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.448-451
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• 2002

This study will offer multi-feature recognition instead of an using mono-feature to improve the accuracy of recognition. Each Feature can be found by following ways. For a face, the feature is calculated by the principal component analysis with wavelet multiresolution. For a lip, a filter is used to find out on equation to calculate the edges of the lips first. Then the other feature is calculated by the distance ratio of facial parameters. We've sorted backpropagation neural network and experimented with the inputs used above and then based on the experimental results we discuss the advantage and efficiency.