• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.495-503
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• 2000

스트레스 심전계에서 발생되는 근잡음을 제거하기 위하여 wavelet interpolation filter(WIF)를 설계하였다. WIF는 크게 웨이브렛 변환부와 보간법 적용부로 구성되어 있다. 웨이브렛 변환부는 Haar 웨이브렛을 이용하였으며 심전도 저주파 영역과 고주파 영역으로 분할하는 과정이다. 보간법 적용부에서는 분할되어진 신호 중 A3을 선택하여 신호의 재생 성능을 향상시키기 위하여 보간법을 적용하였다. WIF의 성능을 평가하기 위해서 신호대 잡음비, 재생신호 자승오차 및 표준편차의 파라미터를 이용하였다. 본 실험에서는 MIT/BIH 부정맥 데이터베이스, European ST-T 데이터베이스 및 삼각파형을 이용하여 성능 파라미터를 측정하였다. 결과적으로 WIF는 성능 파라미터에서 기존에 많이 사용되고 있는 평균값 필터, 중간값 필터 및 hard thresholding 방법에 비해 우수함을 알 수 있었다.

• Journal of Korea Multimedia Society
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• v.9 no.4
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• pp.443-450
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• 2006

In digital image communication systems, YUV 4:2:0 or YUV 4:2:2 images filtered and subsampled versions of YUV 4:4:4 images are utilized and these images recover their size by an interpolation filter. Low pass filters and interpolation filters in the image communication systems are generally utilized. Thus, to improve the image quality, efficient low pass filters and interpolation filters are still required. In this paper, we propose new and efficient low pass filters and interpolation filters and their design method. The low pass filters and interpolation filters used in the MPEG-2 system were developed independently. We utilize wavelet transforms to jointly design low pass filters and interpolation filters. Simulation results show that the proposed filters are superior to the filters used in MPEG-2 in terms of PSNR. In addition, the length of the proposed interpolation filters is shorter than that of the filters used in the MPEG 2 system.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.293-294
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• 2006

Interscale dependency and Liptschitz regularity of the wavelet coefficients imply the existence of functional mapping between scales. In this paper, the neural networks are exploited to learn an intercale mapping. We apply a phase-shifting filter for effective learning of the neural networks.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.19-26
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• 2005

Image interpolation in the wavelet domain usually takes advantage of the probabilistic models for the intrascale statistics and the interscale dependency. In this paper, we adopt the linear model for the absolute values of wavelet coefficients of interpolated image across scale to estimate the variances of extrapolated bands. The proposed algorithm uses randomly generated wavelet coefficients based on the estimated parameters for probabilistic model. Random number generation according to the estimated probabilistic model may induce the 'salt and pepper' noise in subbands. We reduce the noise power by Wiener filtering. We observe that the proposed method generates the histogram of the subband coefficients similar to the that of original image. Experimental results show that our method outperforms the previous wavelet-domain interpolation method as well as the conventional bicubic method.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1170-1178
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• 2021

This paper proposed a versatile algorithm based on a dual-tree complex wavelet transform for intensifying the visual aspect of medical images. First, the decomposition of the input image into a high sub-band and low-sub-band image is done. Further, to improve the resolution of the resulting image, the high sub-band image is interpolated using Lanczos interpolation. Also, contrast enhancement is performed by singular value decomposition (SVD). Finally, the image reconstruction is achieved by using an inverse wavelet transform. Then, the Gaussian filter will improve the visual quality of the image. We have collected images from the hospital and the internet for quantitative and qualitative analysis. These images act as a reference image for comparing the effectiveness of the proposed algorithm with the existing state-of-the-art. We have divided the proposed algorithm into several stages: preprocessing, contrast enhancement, resolution enhancement, and visual quality enhancement. Both analyses show the proposed algorithm's effectiveness compared to existing methods.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.2
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• pp.61-70
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• 2020

Convolutional Neural Networks (CNN) have been used extensively in recent times to solve image classification and segmentation problems. However, the use of CNNs in image super-resolution problems remains largely unexploited. Filter interpolation and prediction model methods are the most commonly used algorithms in super-resolution algorithm implementations. The major limitation in the above named methods is that images become totally blurred and a lot of the edge information are lost. In this paper, we analyze super resolution based on CNN and the wavelet transform super resolution method. We compare and analyze the performance according to the number of layers and the training data of the CNN.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.41-48
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• 2010

In this paper, we present an efficient demosaicking method using the difference between color channels in the wavelet domain. In our method, the low frequency wavelet coefficients are obtained by an edge-directive interpolation using the observed high frequency coefficients. The missing high frequency coefficients are obtained by the estimated low frequency coefficients. In order to reduce artifacts in high frequency domain and to improve visual quality, we update the high frequency coefficient using the color difference rule in the wavelet domain. We simulate our demosaicking method in the wavelet domain and compare our algorithm to the existing demosaicking schemes. Experimental results illustrate that the proposed method can generate enhanced demosaicking results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6A
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• pp.464-472
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• 2012

In this paper, we propose a new super-resolution algorithm that uses successive frames by applying the block matching motion estimation algorithm. Usually, single frame super-resolution algorithms are based on probability or discrete wavelet transform (DWT) approach to extract high-frequency components of the input image, but only limited information is available for these algorithms. To solve this problem, various multiple-frame based super-resolution algorithms are proposed. The accuracy of registration between frames is a very important factor for the good performance of an algorithm. We therefore propose an algorithm using 6-Tap FIR filter to increase the accuracy of the image registration with sub-pixel unit. Proposed algorithm shows better performance than other conventional interpolation based algorithms such as nearest neighborhood, bi-linear and bi-cubic methods and results in about the same image quality as DWT based super-resolution algorithm.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.933-936
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• 2005

Applications of LCD panel are getting more increased for motion-image applications. However, when the motion-images are displayed on LCD panels, they may be blurred due to slow response time of liquid crystal (LC). One of the solutions of the problem is overdrive technique. The technique has a lot of memory usage. In this paper, we propose a reduction method of the frame memory that is required for LCD overdrive. Proposed overdrive architecture consists of line-based lifting integer (5, 3) DWT filter for image data reduction and BLI (Bi-Linearly Interpolation) LUT for pixel value accelerating.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.3
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• pp.46-54
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• 2008

Most commercial CCD/CMOS image sensors have CFA(Color Filter Array) where each pixel gathers light of a selective color to reduce the sensor size and cost. There are many algorithms proposed to reconstruct the original clolr image by adopting pettern recognition of regularization methods to name a few. However the resulting image still suffer from errors such as flase color, zipper effect. In this paper we propose an adaptive edge weight demosaicking algorithm that is based on POCS(Projection Onto Convex Sets) not only to improve the entire image's PSNR but also to reduce the edge region's errors that affect subjective image quality. As a result, the proposed algorithm reconstruct better quality images especially at the edge region.