• 전자공학회논문지 IE
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• v.43 no.4
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• pp.65-75
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• 2006

This thesis deals with a wavelet-based method of denoising of infrared images contaminated with impulse noise and Gaussian noise, he method of thresholding the wavelet coefficients using derivatives and median absolute deviations of the wavelet coefficients of the detail subbands was proposed to effectively denoise infrared images with noises. Particularly, in order to eliminate the impulse noise the method of generating binary masks indicating locations of the impulse noise was selected. By this method, the threshold values dividing edges and noises were obtained more effectively proving the validity of the denoising method compared with the conventional wavelet shrinkage method.

• The Journal of the Korea Contents Association
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• v.4 no.1
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• pp.18-23
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• 2004

In this paper, we propose a new visual feature extraction method for content-based image retrieval(CBIR) based on wavelet transform which has both spatial-frequency characteristic and multi-resolution characteristic. We extract visual features for each frequency band in wavelet transformation and use them to CBIR. The lowest frequency band involves spacial information of original image. We extract L feature vectors using fuzzy homogeneity in the wavelet domain, which consider both the wavelet coefficients and the spacial information of each coefficient. Also, we extract 3 feature vectors wing the energy values of high frequency bands, and store those to image database. As a query, we retrieve the most similar image from image database according to the 10 largest homograms(normalized fuzzy homogeneity vectors) and 3 energy values. Simulation results show that the proposed method has good accuracy in image retrieval using 90 texture images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.31-37
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• 2004

Wavelet transform is a useful tool for analysis and process of image. This showed good performance in image compression and noise reduction. Wavelet coefficients can be effectively modeled by hidden Markov tree(HMT) model. However, in application of HMT model to image interpolation, training procedure is needed. Moreover, the parameters obtained from training procedure do not match input image well. In this paper, the structure of HMT is used for image interpolation, and the parameters of HMT are obtained from statistical characteristics across wavelet subbands without training procedure. In the proposed method, wavelet coefficient is modeled as Gaussian mixture model(GMM). In GMM, state transition probabilities are determined from statistical transition characteristic of coefficient across subbands, and the variance of each state is estimated using the property of exponential decay of wavelet coefficient. In simulation, the proposed method shows improvement of performance compared with conventional bicubic method and the method using HMT model with training.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.1-9
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• 2011

This paper presents a novel scratch detection and removal approach for old film images in wavelet-domain. Various scratch detection and removal algorithms have been proposed for past decades. However, accurate scratch detection and removal with a moderate amount of computing effort is still a problem because of trade off between the quality of the film and computational load. For overcoming this problem, we first decompose an input image using a 3-level wavelet transform, and then remove the scratch by shrinking wavelet coefficients using linear interpolation. Experimental results show that the proposed algorithm can efficiently detect and remove the scratch in damaged films, and also be incorporated into old film restoration systems.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.152-159
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• 2014

In this paper, we introduce a run-length for wavelet coefficients and present a image splicing detection method using the statistical moments for the wavelet run-length. Various pre-processings for the suspicious image are performed to emphasize the discontinuous edges caused by the image splicing. The proposed scheme has the merit that can exploit the various statistical characteristics of the wavelet transform. We extracted up to 72 features, and performed training and testing using SVM(support vector machine). Experimental results showed that the proposed method generates similar detection results compared to the existing methods. In addition, we showed the wavelet domain run-length is useful to detect the spliced image.

• Journal of Broadcast Engineering
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• v.7 no.1
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• pp.55-65
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• 2002

Wavelet transform has recently emerged as a promising technique for video processing applications due to its flexibility in representing non-stationary video signals. Motion estimation which uses wavelet transform of octave band division method is applied In many places but if motion estimation error happens in the lowest frequency band. motion estimation error is accumulated by next time strep and there has the Problem that time and the data amount that are cost In calculation at each steps are increased. On the other hand. wavelet packet that achieved the best image quality in a given bit rate from a rate-distortion sense is suggested. But, this method has the disadvantage of time costs on designing wavelet packet. In order to solve this problem we solved this problem by introducing Top_down method. But we did not find the optimum solution in a given butt rate. That image variance can represent image complexity is considered in this paper. In this paper. we propose a fast multiresolution motion estimation scheme based on the adaptive wavelet transform for video compression.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.4
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• pp.255-265
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• 2007

In case of wavelet coefficients have correlation, in image signal denoising using wavelet shrinkage denoising method, the denoising effect for the image signal is reduced when the wavelet shrinkage denoising method is used. The coefficients of multiwavelet transform have correlation by pre-filters. To solve the degradation problem in multiwavelet transform, V Sterela suggested a new pre-filter for the Universal threshold or weighting factors to the threshold. In this paper, to improve the denoising effect in the multiwavelet transform, the coefficient normalizing method that the coefficient are divided by estimated noise deviation is adopted to the transformed multiwavelet coefficients in the course of wavelet shrinkage technique. And the thresholds of universal, SURE and GCV are estimated using normalized coefficients and tried to denoise by the wavelet shrinkage technique. We compared PSNRs of denoised images for each thresholds and confirmed the efficiency of the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.451-454
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• 2005

Recently, many methods to analyze signal have been proposed and representative methods are the Fourier transform and wavelet transform. In these methods, the Fourier transform represents signal with combination cosine and sine at all locations in the frequency domain. However, it doesn't provide time information that particular frequency occurs in signal and depends on only the global feature of the signal. So, to improve these points the wavelet transform which is capable of multiresolution analysis has been applied to many fields such as speech processing, image processing and computer vision. And the wavelet transform, which uses changing window according to scale parameter, presents time-frequency localization. In this paper, we proposed a new approach using a wavelet of cosine and sine type and analyzed features of signal in a limited point of frequency-time plane.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.7C
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• pp.627-634
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• 2007

When a higher-order modulation scheme (16QAM or 64QAM) is applied to the communications system using the nonlinear high power amplifier (HPA), the performance can be degraded by the nonlinear distortion of the HPA. The nonlinear distortion can be compensated by the adaptive nonlinear Volterra equalizer using the low-complexity LMS algorithm at the receiver. However, the LMS algorithm shows very slow convergence performance. So, in this paper, the parallel M-band discrete wavelet transformed LMS algorithm is proposed in order to improve the convergence speed. Throughout the computer simulations, it is shown that the convergence performance of the proposed method is superior to that of the conventional time-domain and transform-domain LMS algorithms.

• 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.