• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.21-28
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• 2007

Using multi-resolution, the mutiresolution proportional-integral-derivative(MRPID) controller functions as a filter to eliminate noise and disturbance which are included in error signals. If the sampling frequency is high, the response time will be delayed because of the remaining high frequency component although the overshoot is removed. However, if the sampling frequency is low, the response time will be enhanced by getting rid of signal components while the overshoot is increased. In this paper, the sampling frequency tuning method is used the response of the proportional integral derivative(PID) controller and the MRPID controller, and the parameter tuning method is considered the characteristic of the MRPID controller. The proposal method is verified by computer simulations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.10
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• pp.3928-3934
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• 2010

To restore image degraded by motion blur and additive noise, In conventional method, regularization is usually applied to all over the image without considering the local characteristics of image. As a result, ringing artifacts appear in edge regions and the noise amplification is introduced in flat regions. To solve this problem we propose an adaptive regularization iterative restoration using wavelet directional considering edges and the regularization operator with no direction for flat regions. We verified that the proposed method showed results in the suppression of the noise amplification in flat regions, and introduced less ringing artifacts in edge regions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.422-423
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• 2018

In this paper, we implement Frenelet transform for decomposition of the fringe pattern and analyze its characteristics. The implemented wavelet-like basis functions are well suited for reconstruction and processing of optically generated Fresnel holograms. After analyzing the characteristics of the B-spline function, we will discuss the wavelet-like multi-resolution analysis method. Through this process, we implemented a transform tool that can decompose fringe patterns effectively. We have implemented a B-spline function with various decomposition properties and showed the results of decomposing the fringe pattern.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3C
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• pp.387-397
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• 2004

The wavelet transform providing both of the frequency and spatial information of an image is proved to be very much effective for the compression of images, and recently lot of studies on coding algorithms for images decomposed by the wavelet transform together with the multi-resolution theory are going on. This paper proposes a quadtree decomposition method of image compression applied to the images decomposed by wavelet transform by using the correlations between pixels and '0'data grouping. Since the coefficients obtained by the wavelet transform have high correlations between scales and high concentrations, the quadtree method can reduce the data quantity effectively. the experimental infrared image with 256${\times}$256 size and 8〔bit〕, was used to compare the performances of the existing and the proposed compression methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.397-404
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• 2002

In this paper, it is proposed wavelet neural network using the fuzzy concept with the fuzzy and the multi-resolution analysis(MRA) of wavelet transform. Also, it wishes to improve any nonlinear function learning approximation using this system. Here, the fuzzy concept is used the bell type fuzzy membership function. And the composition of wavelet has a unit size. It is used the backpropagation algorithm for learning of wavelet neural network using the fuzzy concept. It is used the multi-resolution analysis of wavelet transform, the bell type fuzzy membership function and the backpropagation algorithm for learning. This structure is confirmed to be improved approximation performance than the conventional algorithms from one dimension and two dimensions function through simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.743-748
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• 2009

In this paper, we propose an effective method to extract background components in automated vision inspection system for polarized film used in TFT LCD display panels. The test image signals are typically composed of three components such as ununiform background, random noises and target defect signals. It is important to analyze the background signal for accurate extraction of defect components. Two dimensional continuous wavelets with first derivative gaussian is used. This methods can be applied for reliable extraction of defect signal by elimination of the background signal from the original image. The proposed method outperforms over conventional FFT methods.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.7
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• pp.134-135
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• 1999

This paper presents a speech enhancement method with spectral subtraction using wavelet, wavelet packet and cosine packet transforms which are known as multiresolutional signal analysis method. The performance of each method is compared with the conventional spectral subtraction method. Performance assessments based on average SNR, cepstral distance and informal subjective listening test are carried out. Experimental result demonstrate that cosine packet shows the best result in objective performance measure as well as subjective shows less musical noise than the conventional spectral subtraction method after removing the noise components.

• The KIPS Transactions:PartA
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• v.14A no.2
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• pp.77-84
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• 2007

The intima media thickness(IMT) of the carotid artery from B mode ultrasound images has recently been proposed as the most useful index of individual atherosclerosis and can be used to predict major cardiovascular events. Ultrasonic measurements of the IMT are conventionally obtained by manually tracing interfaces between tissue layers. The drawbacks of this method are the inter and intra observer variability and its inefficiency. In this paper, we present a multiresolution snake method combined with the dynamic programming, which overcomes the various noises and sensitivity to initialization of conventional snake. First, an image pyramid is constructed using the Gaussian pyramid that maintains global edge information with smoothing in the images, and then the boundaries are automatically detected in the lowest resolution level by minimizing a cost function based on dynamic programming. The cost function includes cost terms which are representing image features and geometrical continuity of the vessel interfaces. Since the detected boundaries are selected as initial contour of the snake for the next level, this automated approach solves the problem of the initialization. Moreover, the proposed snake improves the problem of converging th the local minima by defining the external energy based on multiple image features. In this paper, our method has been validated by computing the correlation between manual and automatic measurements. This automated detection method has obtained more accurate and reproducible results than conventional edge detection by considering multiple image features.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.55-66
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• 2006

We propose a progressive image retrieval method based on an efficient combination of multiresolution color and torture features in wavelet transform domain. As a color feature, color autocorrelogram of the hue and saturation components is chosen. As texture features, BDIP and BVLC moments of the value component are chosen. For the selected features, we obtain multiresolution feature vectors which are extracted from all decomposition levels in wavelet domain. The multiresolution feature vectors of the color and texture features are efficiently combined by the normalization depending on their dimensions and standard deviation vector, respectively, vector components of the features are efficiently quantized in consideration of their storage space, and computational complexity in similarity computation is reduced by using progressive retrieval strategy. Experimental results show that the proposed method yields average $15\%$ better performance in precision vs. recall and average 0.2 in ANMRR than the methods using color histogram color autocorrelogram SCD, CSD, wavelet moments, EHD, BDIP and BVLC moments, and combination of color histogram and wavelet moments, respectively. Specially, the proposed method shows an excellent performance over the other methods in image DBs contained images of various resolutions.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.56-68
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• 2022

In this paper, we propose a compression method for multi-resolution feature maps for VCM. The proposed compression method removes the redundancy between the channels and resolution levels of the multi-resolution feature map through PCA-based transformation. According to each characteristic, the basis vectors and mean vector used for transformation, and the transformation coefficient obtained through the transformation are compressed using a VVC-based coder and DeepCABAC. In order to evaluate performance of the proposed method, the object detection performance was measured for the OpenImageV6 and COCO 2017 validation set, and the BD-rate of MPEG-VCM anchor and feature map compression anchor proposed in this paper was compared using bpp and mAP. As a result of the experiment, the proposed method shows a 25.71% BD-rate performance improvement compared to feature map compression anchor in OpenImageV6. Furthermore, for large objects of the COCO 2017 validation set, the BD-rate performance is improved by up to 43.72% compared to the MPEG-VCM anchor.