• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.603-605
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• 1997

A new algorithm using 1-dimensional wavelet transform for autofocusing of optical instrument has been developed. Previous studies based on the conventional frequency analysis have shown that as the lens-object distance approaches the optimum value, the high frequency energy in the corresponding image shows a consistent increase. However, as conventional frequency analysis techniques hide spatial distribution of each band energy, shape information in the original signal cannot be easily utilized. In this paper, a newly devised wavelet based focus measuring scheme is presented. Unlike other frequency domain analysis techniques that simply produce "frequency-only" spectra, wavelet analysis provides a "time-frequency" localized view of a given signal. As a result, both frequency band filtering and spatial distribution filtering can easily be realized. Depending on the proposed focus quality measuring algorithm, a fast and reliable automatic focus adjustment of optical devices could be implemented.

• ETRI Journal
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• v.28 no.1
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• pp.51-58
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• 2006

In this paper, we propose a method of applying a lifting-based wavelet domain e-median filter (LBWDEMF) for image restoration. LBWDEMF helps in reducing the number of computations. An e-median filter is a type of modified median filter that processes each pixel of the output of a standard median filter in a binary manner, keeping the output of the median filter unchanged or replacing it with the original pixel value. Binary decision-making is controlled by comparing the absolute difference of the median filter output and the original image to a preset threshold. In addition, the advantage of LBWDEMF is that probabilities of encountering root images are spread over sub-band images, and therefore the e-median filter is unlikely to encounter root images at an early stage of iterations and generates a better result as iteration increases. The proposed method transforms an image into the wavelet domain using lifting-based wavelet filters, then applies an e-median filter in the wavelet domain, transforms the result into the spatial domain, and finally goes through one spatial domain e-median filter to produce the final restored image. Moreover, in order to validate the effectiveness of the proposed method we compare the result obtained using the proposed method to those using a spatial domain median filter (SDMF), spatial domain e-median filter (SDEMF), and wavelet thresholding method. Experimental results show that the proposed method is superior to SDMF, SDEMF, and wavelet thresholding in terms of image restoration.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.487-490
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• 1999

In this paper we propose an efficient content-based image retrieval method using the color and wavelet based features. The color features are extracted from color histograms of the global image and the wavelet based features are extracted from the invariant moments of the high-pass band image through the spatial-frequency analysis of the wavelet transform. The proposed algorithm, called color and wavelet features based query(CWBQ), is composed of two-step query operations for efficient image retrieval: the coarse level filtering operation and the fine level matching operation. In the first filtering operation, the color histogram feature is used to filter out the dissimilar images quickly from a large image database. The second matching operation applies the wavelet based feature to the retained set of images to retrieve all relevant images successfully. The experimental results show that the proposed algorithm yields more improved retrieval accuracy with computationally efficiency than the previous methods.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.1
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• pp.1-8
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• 2010

Noise removal methods for noisy images have been studied a lot in the domain of spatial and transform filtering. Low pass filtering was initially applied in the spatial domain. Recently, discrete wavelet transform has widely used for image denoising as well as image compression due to an excellent energy compaction and a property of multiresolution. In this paper, Gabor cosine and sine transform which is considered as human visual filter is applied to image denoising areas using soft thresholding technique. GCST is compared with excellent wavelet transform which uses existing soft thresholding methods from PSNR point of view. Resultant images removed noises are also visually compared. Experimental results with adding four different standard deviation levels of Gaussian distributed noises to real images show that the proposed transform has better PSNR performance of a maximum of 1.18 dB and visible perception than wavelet transform.

• 한국지구물리탐사학회:학술대회논문집
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• 2006.06a
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• pp.51-59
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• 2006

The surface wave data obtained in a tidal flat located in the sw coast of the Korean Peninsula were used to analyse the shear wave velocity structure of the area. First, the phase velocity dispersion curves were obtained by the tau-p stacking method and the group velocity dispersion curves by a wavelet transform method and the Multiple Filtering Technique by Dziewonski. The phase velocity dispersion curves exhibited bigger errors than the group velocity curves. The results showed that the wavelet transform method was more effective in separating the fundamental and the 1st higher mode group velocity curves than the Multiple Filtering Technique. Combined use of the fundamental and the 1st higher mode group velocity dispersion curves in the inversion for the shear wave velocity structure gave better spatial resolution compared when the fundamental mode group velocity was used alone. This study indicates that the group velocity dispersion curves can be used in the inversion of Rayleigh waves for the shear wave velocity structure, especially effectively with the higher mode group velocity curves together.

• Journal of the Korean earth science society
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• v.28 no.7
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• pp.903-913
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• 2007

In this study, the researchers compared the S-wave velocity structures obtained by two kinds of dispersion curves: phase and group dispersions from a tidal flat located in the SW coast of the Korean peninsula. The ${\tau}-p$ stacking method was used for the phase velocity and two different methods (multiple filtering technique: MFT and continuous wavelet transform: CWT) for the phase velocity. It was difficult to separate higher modes from the fundamental mode phase velocities using the ${\tau}-p$ method, whereas the separation of different modes of group velocity were easily achieved by both MFT and CWT. Of the two methods, CWT was found to be more efficient than MFT. The spatial resolutions for the inversion results of the fundamental mode for both phase and group velocities were good for only a very shallow depth of ${\sim}1.5m$. On the other hand, the spatial resolutions were good up to ${\sim}4m$ when both the fundamental and the 1st higher mode poop velocities obtained by CWT were used for S-wave inversion. This implies that the 1st higher mode Rayleigh waves contain more information on the S-wave velocity in deeper subsurface. The researchers applied the CWT method to obtain the fundamental and the 1st higher mode poop velocities of the S-wave velocity structure of a tidal flat located in SW coast of the Korean peninsula. Thea the S-wave velocity structures were compared with the borehole description of the study area.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.131-143
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• 2012

This paper introduces the double-density discrete wavelet transform using 3 direction separable processing method, which is a discrete wavelet transform that combines the double-density discrete wavelet transform and quincunx sampling method, each of which has its own characteristics and advantages. The double-density discrete wavelet transform 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. The dual-tree discrete wavelet transform has a more computationally efficient approach to shift invariance. Also, the dual-tree discrete wavelet transform gives much better directional selectivity when filtering multidimensional signals. But this transformation has more cost complexity Because it needs eight digital filters. Therefor, we need to hybrid transform which has the more directional selection and the lower cost complexity. A solution to this problem is a the double-density discrete wavelet transform using 3 direction separable processing method. The proposed wavelet transformation services good performance in image and video processing fields.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.7
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• pp.1110-1114
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• 2001

The discrete wavelet concept in the k-domain is applied to efficiently represent Green function of integral equations. Application of discrete wavelet concept to Green function in the k-domain can be implemented equivalently by using spatial domain variable-sized windows. The proposed method consists of constant Q-filtering, changing the center of coordinates, and transforming spatially filtered Green functions into those in the k-domain. A mathematical expression of Green function based on the discrete wavelet concept is derived and its characteristics are discussed.

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.188-195
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• 1999

The fMRI signals are composed of many various signals. It is very difficult to find the accurate parameter for the model of fMRI signal containing only neural activity, though we may estimating the signal patterns by the modeling of several signal components. Besides the nose by the physiologic motion, the motion of object and noise of MR instruments make it more difficult to analyze signals of fMRI. Therefore, it is not easy to select an accurate reference data that can accurately reflect neural activity, and the method of an analysis of various signal patterns containing the information of neural activity is an issue of the post-processing methods for fMRI. In the present study, fMRI data was analyzed with the Independent Component Analysis(ICA) method that doesn't need a priori-knowledge or reference data. ICA can be more effective over the analytic method using cross-correlation analysis and can separate the signal patterns of the signals with delayed response or motion related components. The Principal component Analysis (PCA) threshold, wavelet spatial filtering and analysis of a part of whole images can be used for the reduction of the freedom of data before ICA analysis, and these preceding analyses may be useful for a more effective analysis. As a result, ICA method will be effective for the degree of freedom of the data.

• Journal of the Korea Society of Computer and Information
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• v.8 no.4
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• pp.7-12
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• 2003

This paper propose the 1-pass spatially adaptive wavelet thresholding for image denosing. The method of wavelet thresholding for denosing, has been concentrated on finding the best uniform threshold or best basis. However, not much has been done to make this method adaptive to spatially changing statistics which is typical of a large class of images. This spatially adaptive thresholding is extended to the overcomplete wavelet expansion, which yields better results than the orthogonal transform. Experiments show that this proposed method does indeed remove noise significantly, especially for large noise power. Experimental results show that the proposed method outperforms level dependent thresholding techniques and is comparable to spatial Wiener filtering method, 2-pass spatially adaptive wavelet thresholding method in matlab.