• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.124-130
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• 2006

A wavelet-based image compression system allowing both lossless and lossy image compression is proposed in this paper. The proposed algorithm consists of the two stages. The first stage uses the wavelet packet transform and the quad-tree coding scheme for the lossy compression. In the second stage, the residue image taken between the original image and the lossy reconstruction image is coded for the lossless image compression by using the integer wavelet transform and the context based predictive technique with feedback error. The proposed wavelet-based algorithm, allowing an optional lossless reconstruction of a given image, transmits progressively image materials and chooses an appropriate wavelet filter in each stage. The lossy compression result of the proposed algorithm improves up to the maximum 1 dB PSNR performance of the high frequency image, compared to that of JPEG-2000 algorithm and that of S+P algorithm. In addition, the lossless compression result of the proposed algorithm improves up to the maximum 0.39 compression rates of the high frequency image, compared to that of the existing algorithm.

• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.437-444
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• 2008

Land-use or land-cover classification of satellite images is one of the important tasks in remote sensing application and many researchers have tried to enhance classification accuracy. Previous studies have shown that the classification technique based on wavelet transform is more effective than traditional techniques based on original pixel values, especially in complicated imagery. Various basis functions such as Haar, daubechies, coiflets and symlets are mainly used in 20 image processing based on wavelet transform. Selecting adequate wavelet is very important because different results could be obtained according to the type of basis function in classification. However, it is not easy to choose the basis function which is effective to improve classification accuracy. In this study, we first computed the wavelet coefficients of satellite image using ten different basis functions, and then classified images. After evaluating classification results, we tried to ascertain which basis function is the most effective for image classification. We also tried to see if the optimum basis function is decided by energy parameter before classifying the image using all basis functions. The energy parameters of wavelet detail bands and overall accuracy are clearly correlated. The decision of optimum basis function using energy parameter in the wavelet based image classification is expected to be helpful for saving time and improving classification accuracy effectively.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.728-731
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• 1997

This paper discusses approximation modelling of discrete-time linear time-varying system(LTVS). The wavelet transform is considered as a tool for representing and approximating a LTVS. The joint time-frequency properties of wave analysis are appropriate for describing the LTVS. Simulation results is included to illustrate the potential application of the technique.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.568-570
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• 1998

본 연구는 Wavelet Transform을 이미지 처리에 적용하여 지적재산권 보호를 위한 Watermarking 기술을 연구하였다. Watermark가 이미지에 Invisible하게 삽입되면서 압축, Filtering, truncation등과 같은 이미지 처리에도 강력한 Watermark 기술 연구에 중점을 두었다. 특히 완벽한 복원을 위하여 Wavelet Transform을 사용하였고, 또한 DCT기술을 접목시킴으로 해서 압축에 강력한 결과를 나타내게 되었다.

• Broadcasting and Media Magazine
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• v.11 no.1
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• pp.86-92
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• 2006

점차 사용 가능한 대역이 포화됨에 따라 이미 할당된 스펙트럼 자원을 효율적으로 공유하고자 하는 무선인지(cognitive radio) 기술이 대역포화 문제를 해결할 수 있는 주요한 기술로 등장하고있다. 이 원고에서는 광대역 무선인지 기술에 필요한 물리계층에서의 필요조건을 살펴보고 이에 적합한 변조방식에 대해 살펴본다. 특히 광대역 무선인지 기술에 필요한 유연성을 제공할 수 있고 인접채널 간섭을 크게 감소시킬 수 있는 웨이블릿 패킷 변조(wavelet packet modulation: WPM) 기술에 대해 소개한다.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2905-2907
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• 1999

The meaningful speech sound block classification provides very important information in the speech recognition. The following technique of the classification is based on the DWT (discrete wavelet transform), which will provide a more fast algorithm and a useful, compact solution for the pre-processing of speech recognition. The algorithm is implemented to the unvoiced/voiced classification and the denoising.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.10
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• pp.976-983
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• 2006

Multi-resolution Signal Decomposition (MSD) Technique of Wavelet Transform has interesting properties of capturing the embedded horizontal, vertical and diagonal variations within an image in a separable form. This feature was exploited to identify individual partial discharge sources present in multi-source PD pattern, usually encountered during practical PD measurement. Employing the Daubechies wavelet, feature were extracted from the third level decomposed and reconstructed horizontal and vertical component images. These features were found to contain the necessary discriminating information corresponding to the individual PD sources and multi-PD soruces.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.113-116
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• 2000

In this paper, we present new scheme for image coding which efficiently use the relationship between the properties of spatial image and its wavelet transform. Firstly an original image is decomposed into several layers by the wavelet transform, and simultaneously decomposed into 2$\^$n/ ${\times}$ 2$\^$n/ blocks. Each block is classified into 3 regions according to their property, i.e., low activity region(LAR), midrange activity region(MAR), high activity region(HAR). Secondly we are applied texture modeling technique to LAR, MAR and HAR are encoded by Stack-Run coding technique. Finally our scheme Is superior to the Zerotree method in both reconstructed image Quality and transmitted bit rates.

• The Transactions of the Korea Information Processing Society
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• v.7 no.6
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• pp.1980-1987
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• 2000

In this paper, an efficient watermarking technique in wavelet transform domain is proposed. Watermarking is embedding a digital signal called as 'watermark' into images to claim the ownership. In the proposed method, the image is 1-level wavelet transformed, and then the watermark with a binary stamp is embedded into the baseband. The watermark is embedded by inverting the polarities of he selected coefficient paris. In the inverting process, we can increase image quality by finding means and differences of the selected neighboring coefficient paris, and then adding values, which are inversely proportional to the differences, to th means. The experimental results show that the proposed method has good quality and is robust to various attacks such as the JPEG lossy comparison, noise addition, clipping, blurring, etc.