• Journal of information and communication convergence engineering
• /
• v.19 no.1
• /
• pp.54-60
• /
• 2021

Various digital devices were supplied throughout the Fourth Industrial Revolution. Accordingly, the importance of data processing has increased. Data processing significantly affects equipment reliability. Thus, the importance of data processing has increased, and various studies have been conducted on this topic. This study proposes a modified Gaussian filter algorithm based on a fuzzy membership function. The proposed algorithm calculates the Gaussian filter weight considering the standard deviation of the filtering mask and computes an estimate according to the fuzzy membership function. The final output is calculated by adding or subtracting the Gaussian filter output and estimate. To evaluate the proposed algorithm, simulations were conducted using existing additive white Gaussian noise removal algorithms. The proposed algorithm was then analyzed by comparing the peak signal-to-noise ratio and differential image. The simulation results show that the proposed algorithm has superior noise reduction performance and improved performance compared to the existing method.

• Journal of information and communication convergence engineering
• /
• v.20 no.3
• /
• pp.195-203
• /
• 2022

Noise generated during image acquisition and transmission can negatively impact the results of image processing applications, and noise removal is typically a part of image preprocessing. Denoising techniques combined with nonlocal techniques have received significant attention in recent years, owing to the development of sophisticated hardware and image processing algorithms, much attention has been paid to; however, this approach is relatively poor for edge preservation of fine image details. To address this limitation, the current study combined a steering kernel technique with adaptive masks that can adjust the size according to the noise intensity of an image. The algorithm sets the steering weight based on a similarity comparison, allowing it to respond to edge components more effectively. The proposed algorithm was compared with existing denoising algorithms using quantitative evaluation and enlarged images. The proposed algorithm exhibited good general denoising performance and better performance in edge area processing than existing non-local techniques.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.3
• /
• pp.254-260
• /
• 2019

Recently, the importance of signal processing has become gradually significant, as the frequency of video media increases in various fields. However, numerous kinds of noise generated in the transmission and reception processes can possibly affect the signal information, and the noise removal is for that reason essential as a preprocessing step. In this paper, we propose an algorithm to remove the mixed noise which is composed of impulse noise and AWGN. This algorithm is used for image restoration by noise judgment for efficient noise removal in a complex noise environment, and the noise is removed by considering spatial characteristics and pixel variations. Simulation results show that unlike existing methods, the algorithm has excellent noise cancellation characteristics by minimizing both noise effects and consequently eliminating the mixed noise; for objective judgment, we compared and analyzed the data using PSNR and profile.

• Journal of Digital Convergence
• /
• v.11 no.7
• /
• pp.135-140
• /
• 2013

Noise estimation and detection algorithm to adapt quickly to changing noise environment using the LMS Filter. However, the LMS Filter for noise estimation for a certain period of time and need time to adapt. If the signal changes occur, have the disadvantage of being more adaptive time-consuming. Therefore, noise removal method is proposed to a frame basis AELMS Filter to compensate. In this paper, we split the input signal on a frame basis in noisy environments. Remove the LMS Filter by configuring noise predictions using the mean and variance. Noise, even if the environment changes fast adaptation time to remove the noise. Remove noise and environmental noise and speech input signal is mixed to maintain the unique characteristics of the voice is a way to reduce the damage of voice information. Noise removal method using a frame basis AELMS Filter To evaluate the performance of the noise removal. Experimental results, the attenuation obtained by removing the noise of the changing environment was improved by an average of 6.8dB.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.6
• /
• pp.1167-1173
• /
• 2017

As the society increasingly embraces the high - tech digital information age, the field of image processing becomes progressively more branched out and becoming an imperative field. However, image data is deteriorated due to various causes during transmission and salt and pepper noise is typical. Typical methods for removing salt and pepper noise include CWMF, SWMF, and A-TMF. However, existing methods are somewhat insufficient in their ability to remove noise in salt and pepper noise environments. Therefore, in this paper, after it is determined whether noise removal is needed, the following measures were taken. If the center pixel was non-noise, the original pixel was preserved, If it was noise, we proposed a two - dimensional spline interpolation method and a median filter depending on the noise density of the local mask. For the purpose of objective judgment, we compared the results with that of existing methods and used PSNR (peak signal to noise ratio) as a judgment criterion.

• Korean Journal of Remote Sensing
• /
• v.25 no.3
• /
• pp.215-223
• /
• 2009

The noise removal as a preprocessing was tried with various kinds of wavelet pairs. Wavelet transform for 2D images generally uses the same wavelets as basis functions in horizontal and vertical directions. A method with different wavelets was tried for each direction separately, which gives more precise interpretation of the classification. Total 486 pairs of wavelets from nine basis functions were tried to remove image noises. The classification accuracies before and after the noise removal were compared. Although all kinds of wavelet pairs showed the increased accuracies in classification, there were best and worst wavelet pairs depending on the data sets. Wavelet pairs with low energy percentage of LL band showed the high classification accuracy. A pattern was found in the results that very similar vertical accuracy was distributed for each horizontal ones. Since Haar is the shortest length filter, Haar could be a predictor wavelet to find the good wavelet pairs.

• Journal of Korea Multimedia Society
• /
• v.20 no.10
• /
• pp.1671-1677
• /
• 2017

In this paper, we propose a machine learning method for random noise removal in image deblurring. The proposed method uses a logistic regression to select reliable data to use them, and, at the same time, to exclude data, which seem to be corrupted by random noise, in the deblurring process. The proposed method uses commonly available images as training data. Simulation results show an improved performance of the proposed method, as compared with the median filtering based reliable data selection method.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.11
• /
• pp.138-147
• /
• 1997

The ultrasonic images are corrupted by the granular pattern noise - a speckle noise. The speckle exist in the type of coherent imaging systems, and the speckle is the signal independent and multiplicative noise. In this paepr, we derive two filters using the gradient and symmetry. One is a noise suppression filter which removes noise while preserves the edges. It is named the ASRF-GS (Adaptive Speckle Removal Filer - Gradient and Symmetry). And the other is a edge detection filter which obtains the thin edge map, called the EDUGS(Edge Detection Using Gradient and Symmetry). The performance of the proposed noise suppression filter is evaluated by the IMPV(SNR improvement) and the Speckle Index(SI), and the perforamnce of the edge detection is evaluated by the edge detection error rate. According to the evaluated method, The SI reduced about 0.035, The IMPV improved about 1.265(dB), and the edge detection error rate is about 17.5%.

• International Journal of Contents
• /
• v.5 no.4
• /
• pp.7-12
• /
• 2009

In this paper, the authors showed that the removal of impulse noise in CR images was implemented using variety of median filters and SNR/PSNR measurements. They used three kinds of medical images-hand, skull, and knee- for experimental results. But the noise in CR image was only the impulse noise. In real medical image, the noise of an image would be very different type. Therefore. the lack of experimental results using different noise in CR images is one flaw.

• Particle and aerosol research
• /
• v.7 no.3
• /
• pp.87-97
• /
• 2011

In this study, we organized the test results obtained from the performance tests for the CA certificated air cleaners which had been commercially available in Korea since 2003, and analyzed the correlation among the test parameters such as flow rate, particle collection efficiency, clean air delivery rate (CADR), ozone emission, odor removal efficiency and noise level etc. The noise level of 267 air cleaners were increased as concentrated at the 45, 50, 55 dB, which are the required noise level for CA certification according to flow rate, and ozone emissions from the CA air cleaners were significantly lower than the requirement limit, 50 ppb for 24 hour operation. The average particle collection efficiency and odor removal efficiency were 89.3 and 80.8%, approximately 20% higher than the requirement of CA certification, regardless of flow rates. The particle removal performance of an air cleaner was clearly discriminated by its CADR, and the CADR was obtained with a simple calculation: 0.79 x flow rate. The low differentiation of gas removal performance of air cleaners by the current CA gas test method was improved by 3.2, 751.3, 13.4 times for ammonia, acetic acid, respectively, by adopting the CADR concept and the real time measurement method, FTIR, for gas removal performance test.