• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.455-462
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• 2015

Image media is used for the internet, computers and digital cameras as part of the core services of multimedia. Digital images can be easily acquired and processed, due to the development of digital home appliances and personal computers' application software. However, image degradation occurs by various external causes in the acquisition, processing and transmitting process of digital images, and its main cause is known to be noise. Therefore, this study proposed and conducted the simulation of image restoration filter algorithm that processes impulse noise and Gaussian noise by applying Lagrange interpolation and spatial weighted method according to distance, respectively. The proposed algorithm improved 8.77[dB], 8.83[dB] and 10.02[dB], respectively, compared to existing A-TMF, AWMF and MMF, as a result of processing by applying the damaged Girl images to impulse noise(P=60%) and Gaussian noise(${\sigma}=10$).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.798-801
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• 2014

Nowadays, the demand for multimedia services has grown with the rapid evolution in the digital era. But due to external causes in the process of processing, transmitting and storing image data, the images are damaged. One of the major causes of such damage is known to be noise. Some of the most commonly used methods for removing noise are CWMF(center weighted median filter), A-TMF(alpha-trimmed mean filter) and AWMF(adaptive weighted median filter). However, these filters all leave a bit to be desired in removing noise in a complex noise environment. Therefore this paper suggest an image restoration filter algorithm that first judges the noise and sets a adjustment weight based on the median value and distance of the mask to remove the complex noise. For an objective analysis, the results were compared against existing methods and the PSNR(peak signal to noise ratio) was used as a reference.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1076-1081
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• 2019

In response to the recent development of IT technologies, there are more demands for visual devices such as display. However, noise is generated in the process of sending video data due to various reasons. Noise is the representative noise which is commonly found. While A-TMF, CWMF, and AMF are the typical ways for removing Salt and Pepper noise, the noise is not removed well in high-density noise environment. To remove the noise in the high-density noise environment, this study suggested an algorithm which identifies whether it's noise or not. If it's not a noise, matches the original pixel. If it's a noise, divide the $3{\times}3$ local mask into the area of the element treated and the area of the element to be processed. Then, algorithm proposes to apply different weights for each element to treat it as an average filter. To analyze the performance of the algorithm, this study compared PSNR to compare the algorithm with other existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1431-1436
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• 2020

In modern society, various video devices such as CCTV and black boxes are used for convenience. However, noise is frequently generated in the process of transmitting and receiving video images and video signals photographed at night. If such noise is not eliminated, the problem that the image is difficult to identify is generated. Accordingly, noise elimination of images in the image information is an indispensable step. Salt and Pepper noises are typical impulse noises among image noises. Previous research has been carried out as a method for eliminating noise, and CWMF, MMF and A-TMF are typical methods. In common, such a filter exhibits excellent performance in a low-density noise area, but a disadvantage is that noise elimination performance in a high-density noise area is somewhat insufficient. Accordingly, the proposed algorithm uses the inflection point of the histogram graph to separate areas and remove singular points, and proposes a weighting filter utilizing histogram distribution. PSNR was used for objective judgment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.526-532
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• 2022

With the development of IoT technology, image processing is being utilized in various fields such as image analysis, image recognition, medical industry, and factory automation. Noise is generated in image data from causes such as defect in transmission line. Image noise must be removed because it damages the performance of the image processing application program. Salt and Pepper noise is a representative type of image noise, and various studies have been conducted to remove Salt and Pepper noise. Widely known methods include A-TMF, AFMF, and SDWF. However, as the noise density increases, the performance deteriorates. Thus, this paper proposes an algorithm that performs filtering using a fuzzy logic weight mask only in case of noise after noise determination. In order to prove the noise removal performance of the proposed algorithm, an experiment was performed on images with 10% to 90% noise added and the PSNR was compared.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.989-995
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• 2022

Currently, the demand for image processing is increasing due to the development of IT technology, and active research is being conducted. Since image data generates image noise due to various external causes, and thus degrades the performance of the image, noise removal is essential. Salt and Pepper noise is a representative image noise, and various studies are being conducted to remove it. Existing algorithms include A-TMF, AFMF, LIWF, but these have the disadvantage that their performance is somewhat insufficient. Therefore, in this paper, we propose an algorithm that performs filtering using linear interpolation with effective pixels existing around the central pixel only in case of noise after performing noise judgment in order to efficiently remove salt and pepper noise. In order to judge the performance of the proposed algorithm, it was compared using the processed image of the previously studied algorithm and PSNR.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1846-1852
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• 2021

In modern society, various digital devices are being distributed in a wide range of fields due to the fourth industrial revolution and the development of IoT technology. However, noise is generated in the process of acquiring or transmitting an image, and not only damages the information, but also affects the system, causing errors and incorrect operation. AWGN is a representative noise among image noise. As a method for removing noise, prior research has been conducted, and among them, AF, A-TMF, and MF are the representative methods. Existing filters have a disadvantage that smoothing occurs in areas with high frequency components because it is difficult to consider the characteristics of images. Therefore, the proposed algorithm calculates the standard deviation distribution to effectively eliminate noise even in the high frequency domain, and then calculates the final output by applying the probability density function weight of the Laplace distribution using the curve fitting method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.441-443
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• 2022

Currently, image processing is being used in various fields such as image analysis, image recognition, and factory automation according to the development of IT technology. Salt and pepper noise is generated due to various external factors in the process of acquiring or transmitting an image, which deteriorates the image quality. Therefore, noise removal to improve image quality is essential. Various methods have been proposed to remove salt and pepper noise, and representative examples include AF, MF, and A-TMF. However, the conventional filter has insufficient noise removal performance in a high-density noise environment. Therefore, in this paper, we propose an algorithm for estimating and processing the original pixel by using the modified distance weight filter only in the case of noise, and replacing the original pixel in case of non-noise after performing noise judgment. To evaluate the performance of the proposed algorithm, we compare and analyze it with existing algorithms using PSNR.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.115-117
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• 2021

Currently, as IoT technology develops, monitoring systems are being used in various fields, and image processing is being used in various forms. Image data causes noise due to various causes during the transmission and reception process, and if it is not removed, loss of image information or error propagation occurs. Therefore, denoising images is essential. Typical methods of eliminating Salt and Pepper noise in images include AF, MF, and A-TMF. However, existing methods have the disadvantage of being somewhat inadequate in high-density noise. Therefore, in this paper, we propose an algorithm for determining noise for Salt and Pepper denoising and replacing the central pixel with an original pixel if it is non-noise, and processing the filtering mask by segmenting and averaging it in eight directions. We evaluate the performance by comparing and analyzing the proposed algorithms with existing methods.

• Asian-Australasian Journal of Animal Sciences
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• v.6 no.1
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• pp.91-97
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• 1993

This experiment was carried out to examine the effects of urea-treated rice straw feeding on the growth performance of crossbred Holstein heifers under different feeding conditions. In the first experiment, the animals were given diets having 2 levels of TDN and CP and 3 levels of crude fiber (22, 30 and 36%) which were formulated with urea-treated rice straw and concentrates. Daily weight gain of heifers was not different between 22% and 30% CF diets, but the reduction of TDN or CP level to 90% of the requirements decreased the weight gain. Fiber content of 36% also reduced the body weight gain. The reduction of TDN significantly reduced DM intake and increased feed conversion ratio. Feed cost per kg weight gain was significantly increased by an increase in CF to 36%. In the second experiment, separate feeding and total mixing feeding were compared. There were no significant differences between the two feeding systems in body weight gain although the possibility of superiority in SF to TMF remained. DM intake was not affected by the feeding system, but 30% CF diet gave higher DM intake. Feed cost per kg weight gain was lower in the 30% CF diet.