• Journal of information and communication convergence engineering
• /
• v.9 no.3
• /
• pp.331-335
• /
• 2011

Digital images are often corrupted by impulse noise, and it is very important to remove random-valued impulse noise. Cleaning such noise is far more difficult than cleaning salt and pepper impulse noise. In this paper, we proposed an efficient way to remove random-valued impulse noise from digital images. This novel method comprises two stages. The first stage is to detect the random-valued impulse noise in the image and the pixels are roughly divided into two classes, which are "noise-free pixel" and "noise pixel". Then, the second stage is to eliminate the random-valued impulse noise from the image. In this stage, only the "noise pixels" are processed. The "noise-free pixels" are copied directly to the output image. Simulation results indicated that our method provides a significant improvement over many other existing algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.05a
• /
• pp.333-335
• /
• 2011

In the transmitting process of image processing system, images always be corrupted by impulse noise, especially random-valued impulse noise. So removing the random-valued impulse noise is very important, but it is also one of the most difficult case in image processing. The most famous method is the standard median filter, but at edge, the filter has a special feature which has a tendency to decrease the preserve. As a result, we proposed a filter that detection random-valued impulse noise firstly, next to use efficient method to remove the noise and preserve the details. And through the simulation, we compared with the algorithms and indicated that proposed method significant improvement over many other existing algorithms.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.3
• /
• pp.1383-1391
• /
• 2018

A new nonlinear filtering algorithm for effectively denoising images corrupted by the random-valued impulse noise, called dual sliding statistics switching median (DSSSM) filter is presented in this paper. The proposed DSSSM filter is made up of two subunits; i.e. Impulse noise detection and noise filtering. Initially, the impulse noise detection stage of DSSSM algorithm begins by processing the statistics of a localized detection window in sorted order and non-sorted order, simultaneously. Next, the median of absolute difference (MAD) obtained from both sorted statistics and non-sorted statistics will be further processed in order to classify any possible noise pixels. Subsequently, the filtering stage will replace the detected noise pixels with the estimated median value of the surrounding pixels. In addition, fuzzy based local information is used in the filtering stage to help the filter preserves the edges and details. Extensive simulations results conducted on gray scale images indicate that the DSSSM filter performs significantly better than a number of well-known impulse noise filters existing in literature in terms of noise suppression and detail preservation; with as much as 30% impulse noise corruption rate. Finally, this DSSSM filter is algorithmically simple and suitable to be implemented for electronic imaging products.

• Journal of KSNVE
• /
• v.9 no.4
• /
• pp.778-784
• /
• 1999

A method for obtaining the motion of an impact system whose primary and secondary system are composed of lumped masses, springs and dampers, and all the contacts are made through spring and damping elements is presented. The frequency response functions derived from the equations of motion and the impulse response functions obtained from the inverse Fourier transform of the derived frequency response functions are used for the calculation of the system responses. The procedure developed for the calculation of displacements and force time-histories was based on the convolution integrals of impulse response functions and forces applied to the systems. Time histories of displacements and contact forces are obtained for the case where a random excitation is applied to a point in the system. Impact statistics such as contact forces and the time between impacts calculated from those time histories is presented.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.12
• /
• pp.27-33
• /
• 2016

In this paper, we propose the bilateral filter based on median value that can reduce random noise and impulse noise with minimal loss of contour information. In general, EO / IR camera to generate a random or impulse noise due to a number of reasons. This noise reduces the performance of detecting and tracking by signal processing. To reduce noise, our proposed bilateral filter sorts the values of the target pixel and the peripheral pixels, and extracts a median filter coefficients of the Gaussian type. Then to extract the Gaussian filter coefficient involved with the distance between the center pixel and the surrounding pixels. As using those filter coefficients, our proposed method can remove the various noise effectively while minimizing the loss of the contour information. To validate our proposed method, we present experimental results for several IR images.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.6
• /
• pp.722-728
• /
• 2020

Digital image processing has been applied in a wide range of fields due to the development of IoT technology and plays an important role in data processing. Various techniques have been proposed to remove such noise, but the conventional impulse noise canceling methods are insufficient to remove noise of edge components of an image, and have a disadvantage of being greatly affected by random impulse noise. Therefore, in this paper, we propose an algorithm that effectively removes edge component noise in random impulse noise environment. The proposed algorithm calculates the threshold value by determining the noise level and switches the filtering process by comparing the reference value with the input pixel value. The proposed algorithm shows good performance in the existing method, and the simulation results show that the noise is effectively removed from the edge of the image.

• Journal of the Korea Fashion and Costume Design Association
• /
• v.8 no.1
• /
• pp.13-24
• /
• 2006

The college students selected by random sampling were classified into four groups by their body cathexis and clothing attitude to investigate differences in the consumer's Impulse-Buying in a negative consumer situation. Consumer cluster were classified into four groups: positive congruity(G1), positive incongruity(G2), negative congruity(G3), negative incongruity(G4). The result are as follows: G1 had high impulse. After Impulse-buying in a negative consumer situation they had negative attitude. G2 tends to do Impulse-buying a lot in a negative consumer situation. G3 had low impulse. After impulse-buying in a negative consumer situation they had both positive and negative attitude. After impulse-buying, G4 had negative and they tend to do a pure impulse buying in a negative consumer situation. As a result, the buying-impulse could cause the impulse-buying.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.3
• /
• pp.598-604
• /
• 2012

Image signal is corrupted by various noises in image processing, many studies are being accomplished to restore those images. In this paper, we proposed a cascade filter algorithm for removing random valued impulse noise. The algorithm consists two steps that noise detection and noise elimination. Variance of filtering mask and center pixel variance are calculated for noise detection, and the noise pixel is replaced by estimated value which first apply switching self adaptive weighted median filter and finally processed by modified weight filter. Considering the proposed algorithm only remove noise and preserve the uncorrupted information that the algorithm can not only remove noise well but also preserve edge.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1992.06a
• /
• pp.33-36
• /
• 1992

In this paper we propose the impulse noise-robust Wiener filter based on a combination of Wiener and modified trimmed mean(MTM) filters. The robust Wiener filter uses the trimming operation of the MTM filter to replace the outliers with the median within the window and the new set of samples which can be considered as the random process with same mean are inputted into the following Wiener filter. We show that the robust Wiener filter is effective in frequency selective filtering of nonstationary signals while preserving signal edges with the rejection of impulse noise.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.54 no.3
• /
• pp.166-173
• /
• 2005

Preserving image features, edges and details in the process of impulsive noise filtering is an important problem. To avoid image blurring, only corrupted pixels must be filtered. In this paper, we propose an effective impulse noise detection method using Self-Organizing Neural Network(SONN) which applies median filter selectively for removing random-valued impulse noises while preserving image features, edges and details. Using a $3\times3$ window, we obtain useful local features with which impulse noise patterns are classified. SONN is trained with sample image patterns and each pixel pattern is classified by its local information in the image. The results of the experiments with various images which are the noise range of $5-15\%$ show that our method performs better than other methods which use multiple threshold values for impulse noise detection.