• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.595-607
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• 2010

This paper addresses the problem of reducing the speckle noise in SAR images by wavelet transformation, using a non-local means(NLM) filter originated for Gaussian noise removal. Log-transformed SAR image makes multiplicative speckle noise additive. Thus, non-local means filtering and wavelet thresholding are used to reduce the additive noise, followed by an exponential transformation. NLM filter is an image denoising method that replaces each pixel by a weighted average of all the similarly pixels in the image. But the NLM filter takes an acceptable amount of time to perform the process for all possible pairs of pixels. This paper, also proposes an alternative strategy that uses the t-test more efficiently to eliminate pixel pairs that are dissimilar. Extensive simulations showed that the proposed filter outperforms many existing filters terms of quantitative measures such as PSNR and DSSIM as well as qualitative judgments of image quality and the computational time required to restore images.

• Journal of the Korea Computer Industry Society
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• v.6 no.2
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• pp.271-278
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• 2005

This paper proposed a new time adapted threshold using the standard deviations of Wavelet coefficients after Wavelet transform by frame scale. The time adapted threshold is set up using the sum of standard deviations of Wavelet coefficient in level 3 approximation and weighted level 1 detail. Level 3 approximation coefficients represent the voiced sound with low frequency and level 1 detail coefficients represent the unvoiced sound with high frequency. After reducing noise by soft thresholding with the proposed time adapted threshold, there are still residual noises in silent interval. To reduce residual noises in silent interval, a detection algorithm of silent interval is proposed. From simulation results, it is demonstrated that the proposed algorithm improves SNR and MSE performance more than Wavelet transform and Wavelet packet transform does.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.952-957
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• 2006

The present study applied wavelet transform and artificial neural networks (ANNs) for the prediction of daily TOC data. TOC data were transformed into denoised data by the wavelet transform and the noise-reduced data were used for the prediction model by artificial neural networks. For the application of wavelet transform, Daubechies wavelet of order 10 ('db10') was used as a basis function and decomposed the TOC data up to fifth level with five detail components and one approximation component. ANNs were calibrated with the input data of the segregated TOC data corresponding to the details from second to fifth level and the approximation. Consequently, the ANNs model for the prediction of daily TOC data showed the best result when it had seventeen hidden nodes in its layer.

• Journal of the Korean Society of Radiology
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• v.11 no.1
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• pp.63-68
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• 2017

To reduce the exposure dose in head CT, the use of low tube voltage is required. However, increasing noise may cause errors in the second data processing. In this study, we proposed a method to reduce noise by using low tube voltage. Experimental results show that the noise level is high at 100kVp and lowest at 140 kVp. The dose was lower at 100 kVp and higher at 140 kVp. As a result of applying the wavelet according to the threshold value, the noise value in the wavelet Th30 decreased to 4.51. Using the parameter condition(100 kVp, rotation time 0.5 sec, dose: 40.64 mGy) and the wavelet Th 30, the dose reduction of 65.3% was possible. We believe that applying the proposed method to head CT images will help to patient safety and interpret accurate information.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.510-512
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• 2018

The proposed method enhances the resolution of images by removing noise using wavelet transform to remove noise from images generated by ultrasound diagnosis. We propose an algorithm to reduce the speckle noise and enhance the edge of the ultrasound image. The proposed algorithm can enhance edges of various sizes by using wavelet transform which can use both frequency and spatial information. Experimental results show that the performance of the algorithm for noise reduction of ultrasound images is about 0.45ms for $520{\times}440$ images.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.42-48
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• 2007

In x-ray imaging system, two kinds of noises are involved. First, the charge generated from the radiation interaction with the detector during exposure. Second, the signal is then added by readout electronics noise. But, x-ray images are not modeled by Gaussian noise but as the realization of a Poisson process. In this paper, we apply a new approach to remove Poisson noise from medical X-ray image in the wavelet domain, the applied methods shows more excellent results in cone-beam CT.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1088-1097
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• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

• Journal of radiological science and technology
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• v.46 no.3
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• pp.207-217
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• 2023

Image noise reduction algorithm performs important functions in light microscopy. This study aims to systematically review the research trend of types and performance evaluation methods of noise reduction algorithm in light microscopic images. A systematic literature search of three databases of publications from January 1985 to May 2020 was conducted; of the 139 publications reviewed, 16 were included in this study. For each research result, the subjects were categorized into four major frameworks-1. noise reduction method, 2. imaging technique, 3. imaging type, and 4. evaluation method-and analyzed. Since 2003, related studies have been conducted and published, and the number of papers has increased over the years and begun to decrease since 2016. The most commonly used method of noise reduction algorithm for light microscopy images was wavelet-transform-based technology, which was mostly applied in basic systems. In addition, research on the real experimental image was performed more actively than on the simulation condition, with the main case being to use the comparison parameter as an evaluation method. This systematic review is expected to be extremely useful in the future method of numerically analyzing the noise reduction efficiency of light microscopy images.

• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.153-158
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• 2008

Blocking artefact noise is necessarily happened in compressed images using block-coded algorithms such as JPEC compressing algorithm. This noise is more recognizable especially in highly compressed images. In this paper, an algorithm is presented for reduction of blocking artefact noise using wavelet. Furthermore, we also mention about the median filter which is often used in image processing. Moreover, we compared the algorithm in this paper with the median filter, and its result was much better than the median filter both visually and numerically.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.49-52
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• 2000

This paper is to propose an algorithm to reduce the calculation time to perform the 2-dimensional Discrete Wavelet Transform(2DWT). We call this algorithm as Reduced 2-dimensional Discrete Wavelet Transformation(R2DWT). This algorithm uses a modified Mallat-tree such that in each level, the column transform is performed only with the low-pass filtered row transform result. The resulting number of sub-band regions is 2L＋1, meanwhile the original(2DWT) has 3L＋1 sub-regions, where L is the transform level. To show the proposed algorithm is useful without much loss in SNR(Signal-to-Noise Ratio), we performed experiments with various images. The results showed that above 5:1 in compression ratio, the proposed algorithm has less than 0.SdB difference in SNR from 2DWT with about 25% reduction in calculation time.