• Science of Emotion and Sensibility
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• v.7 no.2
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• pp.141-148
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• 2004

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 cA3 and weighted cDl. cA3 coefficients represent the voiced sound with low frequency and cDl coefficients represent the unvoiced sound with high frequency. From simulation results, it is demonstrated that the proposed algorithm improves SNR and MSE performance more than Wavelet transform and Wavelet packet transform does. Moreover, the reconstructed signals by the proposed algorithm resemble the original signal in terms of plosive sound, fricative sound and affricate sound but Wavelet transform and Wavelet packet transform reduce those sounds seriously.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1103-1108
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• 2023

In this paper, we propose a new algorithm for attenuating the background noises in acoustic signal. This algorithm improves the noise attenuation performance by using the FNN(: Full-connected Neural Network) deep learning algorithm instead of the existing adaptive filter after wavelet transform. After wavelet transforming the input signal for each short-time period, noise is removed from a single input audio signal containing noise by using a 1024-1024-512-neuron FNN deep learning model. This transforms the time-domain voice signal into the time-frequency domain so that the noise characteristics are well expressed, and effectively predicts voice in a noisy environment through supervised learning using the conversion parameter of the pure voice signal for the conversion parameter. In order to verify the performance of the noise reduction system proposed in this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed. As a result of the experiment, the proposed deep learning algorithm improved Mean Square Error (MSE) by 30% compared to the case of using the existing adaptive filter and by 20% compared to the case of using the STFT(: Short-Time Fourier Transform) transform effect was obtained.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.359-366
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• 2018

In this paper, the noise is removed by using CTLS aircraft installed FBG sensor inside the aircraft wing. We suggest a normal wavelet transform scheme with motion - invariant characteristics for noise reduction. In the case of installing FBG sensors inside the composite material as in CTLS, large and small empty spaces and parts or sections are generated between the adhesive layers, and a signal splitting problem occurs. FBG sensor is not affected by noise. but eletromagnetic, light source, light detector and signal processing device are influeced by noise because these are eletronic components what affected by eletromagnetic wave. because of this, errors are occured. Experimental results show that the noise can be removed using normal wavelet transform and more accurate data detection is possible.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.1
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• pp.47-53
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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 block division. Furthermore, we also mention about the median filter which is often used in image processing.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.367-368
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• 2007

In this paper, we propose a new de-noising algorithm for 2 dimensional image using discrete wavelet transform. The proposed algorithm consists of edge detection in spatial domain, zero-tree estimation, subband estimation, and shrinkage algorithm. The results from it shows that the denoised image which Is damaged by 20% gaussian noise has 28dB quality for the original one.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.693-698
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• 2001

Noisy data are often fitted using a smoothing parameter, controlling the importance of two objectives that are opposite to a certain extent. One of these two is smoothness and the other is closeness to the input data. The optimal value of this parameter minimizes the error of the result. This optimum cannot be found exactly, simply because the exact data are unknown. This paper propose the threshold value for noise reduction based on wavelet-thresholding. In the proposed method PSNR results show that the threshold value performs excellently in comparison with conventional methods without knowing the noise variance and volume of signal.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.5
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• pp.73-78
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• 2011

We developed a noise reduction algorithm for the measurement accuracy improvement of a fiber-optic distributed temperaure sensor system. The denoising technique is based on the wavelet transform. The proposed algorithm was applied to a FBG sensor output with the Gaussian line-fitting algorithm to minimize the output noise which originated from the intensity noise of the laser light source and the instability of signal porcessing. We confirmed the feasibility of the denoising algorithm by comparing the measurement results with those obtained with the Gaussian line-fitting algorithm only.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7C
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• pp.937-945
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• 2004

A binary decision model which is used to denoising has demerits to measure the precise ratio of signal to noise because of only a binary classification. To supplement these demerits, complex statistical model and undecimated wavelet transform are generally exploited. In this paper, we propose a noise reduction method using a multi-level decision model for measuring the ratio of noise in noisy image. The propose method achieves good denoising performance with orthogonal wavelet transform because the ratio of signal to noise can be calculated to multi-valued form. In simulation results, the proposed denoising method outperforms 0.1dB in the PSNR sense than the state of art denoising algorithms using orthogonal wavelet transform.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.965-968
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• 2016

This paper describes a new speckle noise reduction methode on the sonar image using TVG Filtering and PDF wavelet transform. The speckle noise makes the degrading image to discriminate the various object on the ocean bed. The TVG filter removes the speckle noise by gain with observing the results timely and inductively. The experimental result is that speckle noise is reduced to 90 %. Thus the proposed technique leads the mage recognition to be enhanced in the submarine environment.

• 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.