• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.69-75
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• 2019

The standard application area of over-sampled discrete wavelet transform is noise removal technology for digital images. Comparing dual density discrete wavelet transform with dual tree discrete wavelet transform, we have almost similar characteristics. In this paper, several discrete wavelet transforms are accomplished on digital image existing with noise, noises are removed with threshold processing algorithm on subband, performance evaluation experiments of the reconstructed images are accomplished. If we decide appropriate threshold value, the effect noise removal is possible. In this paper, we can certified that the suggested algorithm of 3-direction separable processing with 2 dimension dual density discrete wavelet transform is superior to several experiment results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.221-232
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• 2012

The double-density discrete wavelet transform(DWT) is an improvement upon the critically sampled DWT with important additional properties. It employs one scaling function and two distinct wavelets, which are designed to be offset from one another by one half. And it is overcomplete by a factor of two. Also, this transformation is nearly shift-invariant. But there is room for improvement because not all of the wavelets are directional. That is, although the double-density DWT utilizes more wavelets, some lack a dominant spatial orientation, which prevents them from being able to isolate those directions. Proposed method is a DWT that combines the double-density DWT and quincunx sampling, each of which has its own characteristics and advantages. Especially, the quincunx sampling treats the different directions more homogeneously. As a result, since proposed method can generate sub-images of multiple degrees rotated versions, this method provides an improved performance in image processing fields.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.179-191
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• 2013

The quincunx lattice is a non-separable sampling method in image processing. It treats the different directions more homogeneously and good frequency property than the separable two dimensional schemes. The high density discrete wavelet transformation is one that expands an N point signal to M transform coefficients with M > N. In two dimensions, this transform outperforms the standard discrete wavelet transformation in terms of shift-invariant. Although the transformation utilizes more wavelets, sampling rates are high costs. This paper proposed the high density discrete wavelet transform using quincunx sampling, which is a discrete wavelet transformation that combines the high density discrete transformation and non-separable processing method, each of which has its own characteristics and advantages. Proposed wavelet transformation can service good performance in image processing fields.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.1-8
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• 2013

High-density discrete wavelet transformation is one way to overcome the disadvantages of the standard wavelet transform of shift invariant because it increases the number of subband signals. In this paper, high-density discrete wavelet transform consisting of three channels is applied in a two-dimensional image processing. Experimental results show that the proposed method is well satisfied with the shift invariant and is excellent directional selectivity because it could generate many subband images.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.65-74
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• 2012

In this paper, we explore the application of 2-D dual-tree discrete wavelet transform (DDWT), which is a directional and redundant transform, for image coding. DDWT introduces limited redundancy and allows the transform to provide approximate shift invariance and directionally selective filters while preserving the usual properties of perfect reconstruction and computational efficiency with good well-balanced frequency responses. Also, quincunx lattice yields a non separable 2D-wavelet transform, which is also symmetric in both horizontal and vertical direction. And non-separable wavelet transformation can generate sub-images of multiple degrees rotated versions. The proposed 2-D non-separable DDWT can provide efficient approximation for directional features of images schemes, such as edges and contours in images that are not aligned with the horizontal or vertical direction. Finally, non-separable image processing using DDWT services good performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.77-83
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• 2014

Over-sampled discrete wavelet transformation is one way to overcome the disadvantages of the standard wavelet transform of shift invariance even though it increases the number of subband signals. Non-separable based discrete wavelet transform is efficient that it satisfies shift invariance and directional selectivity. In this paper, since efficient over-sampled wavelet transform is possible in a two-dimensional image processing, we show that the proposed method is well applied with performance improvement of digital image and noise removal.

• Journal of Broadcast Engineering
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• v.4 no.2
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• pp.127-133
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• 1999

The conventional fractal image compression based on discrete wavelet transform uses the fixed block size in fractal coding and reduces PSNR at low bit rate. This paper proposes a fractal image coding based on discrete wavelet transform which improves PSNR by using variable block size in fractal coding. In the proposed method. the absolute values of discrete wavelet transform coefficients are computed. and the discrete wavelet transform coefficients of different highpass subbands corresponding to the same spatial block are assembled. and the fractal code for the range block of each range block level is assigned. and then a decision tree C. the set of choices among fractal coding. "0" encoding. and scalar quantization is generated and a set of scalar quantizers q is chosen. And then the wavelet coefficients. fractal codes. and the choice items in the decision tree are entropy coded by using an adaptive arithmetic coder. This proposed method improved PSNR at low bit rate and could achieve a blockless reconstructed image. As the results of experiment. the proposed method obtained better PSNR and higher compression ratio than the conventional fractal coding method and wavelet transform coding.rm coding.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.6
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• pp.604-612
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• 1992

OWT is a tool for block transform coding with wavelet basis functions that overlap adjacent blocks. The OWT can reduce the block effect. Without increasing the transmission data in this paper transform matrix of OWT Is presented. Some simulation results show that performances are early same to BCT, but block effect is reduced to very low level.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.1
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• pp.7-16
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• 2010

Face recognition of face images is an active subject in the area of computer pattern recognition, which has a wide range of potential. Automatic extraction of face image of the feature points is an important step during automatic face recognition. Whether correctly extract the facial feature has a direct influence to the face recognition. In this paper, a new method of facial feature extraction based on Discrete Wavelet Transform is proposed. Firstly, get the face image by using PC Camera. Secondly, decompose the face image using discrete wavelet transform. Finally, we use the horizontal direction, vertical direction projection method to extract the features of human face. According to the results of the features of human face, we can achieve face recognition. The result show that this method could extract feature points of human face quickly and accurately. This system not only can detect the face feature points with great accuracy, but also more robust than the tradition method to locate facial feature image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12A
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• pp.2074-2084
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• 2001

la this paper, a new digital watermarking algorithm using wavelet transform in frequency domain is suggested. The wavelet coefficients of low frequency subband are utilized to embed the watermark, After the original image is transformed using discrete wavelet transform, their coefficients are transformed into efficient1y in Sequency domain. DCT and FFT transforms are utilized in this processing. Watermark image of general image format is transformed using DCT and the hiding watermark into wavelet coefficients is equally distributed in frequency domain. Next, these wavelet coefficients are performed with inverse transform. The detection process of watermark is performed with reverse direction to insertion process. In this paper, we developed core watermark technologies which are a data hiding technology to hide unique logo mark which symbolizes the copyright and a robust protection technology to protect logo data from external attack like as compression, filtering, resampling, cropping. The experimental results show that two suggested watermarking technologies are invisible and robust.