• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.3
• /
• pp.125-131
• /
• 2019

Through the 4th Industrial Revolution, various digital equipments are being distributed, and accordingly, the importance of data processing is increasing. As data processing has a great effect on the reliability of equipment, its importance is increasing, and various studies are being conducted. In this paper, we propose an algorithm to remove AWGN in consideration of the noise in the image. The proposed algorithm is used in the filtering process by inferring the standard deviation of the image noise. The noise is removed by dividing the filter for the high frequency component and the filter for the low frequency component compared with the standard deviation of the filtering mask. The proposed algorithm is simulated with the existing methods for evaluation and compared and analyzed by difference image, PSNR and profile. The proposed algorithm minimizes the effect of noise and preserves the important characteristics of the image and shows the performance of efficient noise removal.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.11
• /
• pp.2717-2723
• /
• 2015

Recently, digital image processing is being applied in various areas of broadcasting, communication, computer graphic and medical science. But, degradation of images occurs in the process of digital image acquisition, processing and transmission. Therefore, in order to remove the mixed noise, this paper suggested the image restoration algorithm to process salt and pepper noise with weighted filters according to 4 direction pixel changes after judging the noise and to process AWGN with weighted filters which have individually different characteristics. Regarding the processed results by applying Boat images which were corrupted by salt and pepper noise(P=40%), suggested algorithm showed the improvement by 1.33[dB], 1.41[dB], 0.51[dB] respectively compared with the existing CWMF, AWMF, MMF.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.35 no.2
• /
• pp.175-181
• /
• 2011

We propose a novel algorithm for measuring the radius of tubes. This proposed algorithm is capable of effectively removing added noise and measuring the radius of tubes within allowable precision. The noise is removed by using a candidate true center that minimizes the standard deviation with respect to the radius. Further, the disconnection in data points resulting from noise removal is solved by using a connection algorithm. The final step of the process is repeated until the value of the standard deviation decreases to a small predefined value. Experiments were performed using circle geometries with added noise and a real tube with complex noise and that is used in the braking units of automobiles. It was concluded that the measurement carried out using the algorithm was accurate within 1.4%, even with 15% added noise.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2012.05a
• /
• pp.26-27
• /
• 2012

Reflection considered as the view disturbing noise in optical systems, such as stereo camera in autonomous vehicles especially in night. Reflection caused by the street light or due to rainwater under adverse weather conditions. A blur image detected by the camera that results in wrong guidance to vehicle for detecting its track. A vehicle guidance approach through stereo vision can be same in day and night time. However it cannot be guided with same image analysis due to diverse illumination conditions. We develop the technique that shows its efficacy with illustrations of reflection removal off the camera lens and vehicle tracking control.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.10
• /
• pp.80-88
• /
• 1997

This paper describes a design of a Wavelet Adaptive Filter(WAF) for the removal of the baseline wandering and the minimization of the signal distortion using by wavelet transform and adaptive filter in the ECG signal. WAF consists of two parts. The first part is wavelet transform that decomposes the ECG signal into seven frequency bands using Vaidyanathan and Hoang wavelet. The second part is adaptive filter that uses the signal of seventh low frequency band among the wavelet transformed signals as primary input and a unit impulse sequence as reference input. For the evaluation of the performance of WAF, we used several baseline wandering elimination filters such as commerical standard filter with cutoff frequency of 0.5Hz and general adaptive filter. We made use of MIT/BIH database and real patient data for the evaluation. In conclusion, WAF showed a lower ST segement distortion than standard filter and adaptive filter and has a higher eliminated noise power than standard filter and adaptive filter.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.5
• /
• pp.21-29
• /
• 2024

Reducing the radiation dose during CT scanning can lower the risk of radiation exposure, but not only does the image resolution significantly deteriorate, but the effectiveness of diagnosis is reduced due to the generation of noise. Therefore, noise removal from CT images is a very important and essential processing process in the image restoration. Until now, there are limitations in removing only the noise by separating the noise and the original signal in the image area. In this paper, we aim to effectively remove noise from CT images using the wavelet transform-based GAN model, that is, the WT-GAN model in the frequency domain. The GAN model used here generates images with noise removed through a U-Net structured generator and a PatchGAN structured discriminator. To evaluate the performance of the WT-GAN model proposed in this paper, experiments were conducted on CT images damaged by various noises, namely Gaussian noise, Poisson noise, and speckle noise. As a result of the performance experiment, the WT-GAN model is better than the traditional filter, that is, the BM3D filter, as well as the existing deep learning models, such as DnCNN, CDAE model, and U-Net GAN model, in qualitative and quantitative measures, that is, PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measure) showed excellent results.

• Economic and Environmental Geology
• /
• v.57 no.1
• /
• pp.51-71
• /
• 2024

Seismic exploration is one of the widely used geophysical exploration methods with various applications such as resource development, geotechnical investigation, and subsurface monitoring. It is essential for interpreting the geological characteristics of subsurface by providing accurate images of stratum structures. Typically, geological features are interpreted by visually analyzing seismic sections. However, recently, quantitative analysis of seismic data has been extensively researched to accurately extract and interpret target geological features. Seismic attribute analysis can provide quantitative information for geological interpretation based on seismic data. Therefore, it is widely used in various fields, including the analysis of oil and gas reservoirs, investigation of fault and fracture, and assessment of shallow gas distributions. However, seismic attribute analysis is sensitive to noise within the seismic data, thus additional noise attenuation is required to enhance the accuracy of the seismic attribute analysis. In this study, four kinds of seismic noise attenuation methods are applied and compared to mitigate random noise of poststack seismic data and enhance the attribute analysis results. FX deconvolution, DSMF, Noise2Noise, and DnCNN are applied to the Youngil Bay high-resolution seismic data to remove seismic random noise. Energy, sweetness, and similarity attributes are calculated from noise-removed seismic data. Subsequently, the characteristics of each noise attenuation method, noise removal results, and seismic attribute analysis results are qualitatively and quantitatively analyzed. Based on the advantages and disadvantages of each noise attenuation method and the characteristics of each seismic attribute analysis, we propose a suitable noise attenuation method to improve the result of seismic attribute analysis.

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

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.05a
• /
• pp.790-792
• /
• 2017

As the demand of digital image processing devices has been rapidly increased recently, the excellent image quality is required. However, degradation can be occurred with multiple causes during transmission and processing process. Therefore, the needs to eliminate the noise are increased and the noise elimination technology became the major study area. Therefore, image restoration algorithm was suggested to apply the filter differently by edge and non-edge areas, using modified edge detection with preprocessing process so as to relieve the effect of additive white Gaussian noise(AWGN) which is added in the image, in this article. In addition, it was compared with the existing methods using peak signal to noise ratio(PSNR) as the objective determination standard of the improvement effect.

• The Journal of the Acoustical Society of Korea
• /
• v.39 no.4
• /
• pp.364-370
• /
• 2020

Rocket noise generated from the exhaust plume produces the enormous acoustic loading, which adversely affects the integrity of the electronic components and payload (satellite) at liftoff. The prediction of rocket noise consists of two steps: the supersonic jet exhaust is simulated by a method of the Computational Fluid Dynamics (CFD), and an acoustic transport method, such as the Helmholtz-Kirchhoff integral, is applied to predict the noise field. One of the difficulties in the CFD step is to remove the boundary reflection artifacts from the finite computation boundary. In general, artificial damping, known as a sponge layer, is added nearby the boundary to attenuate these reflected waves but this layer demands a large computational area and an optimization procedure of related parameters. In this paper, a cost-efficient way to separate the reflected waves based on the two microphone method is firstly introduced and applied to the computation result of a laboratory-scale supersonic jet noise without sponge layers.