• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.724-729
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• 2013

Recently, with the popularization of digital devices, the requirements of image quality is becoming higher and higher. However, the images are frequently corrupted in the image data processing, there are several reasons for this and the noise is considered as the main reason. Therefore, in order to alleviate the influence of AWGN(additive white Gaussian noise) in image, this paper puts forward the spatial weighted filtering algorithm. The algorithm set the weighted value according to the spatial distance, compared with the existing methods. The algorithm not only alleviated the influence of AWGN effectively but also reserved image details.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.217-219
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• 2019

In response to the increased use of digital video device, more researches are actively made on the image processing technologies. Image processing is practically used on various applied fields such as medical photographic interpretation, and object recognition. The types of image noise include Gaussian Noise, Impulse Noise, and Salt and Pepper. Noise refers to the unnecessary information which damages the video and the noise is mainly removed by a filter. Typical noise removal methods are Median Filter and Average Filter. While Median Filter is effective for removing Salt and Pepper noise, the noise removal performance is relatively lower in the environment with high noise density. To address such issue, this study suggested an algorithm which utilizes neighboring pixels to remove noise.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.3
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• pp.49-58
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• 2020

Image interpolation, a technology that converts low resolution images into high resolution images, has been widely used in various image processing fields such as CCTV, web-cam, and medical imaging. This technique is based on the fact that the statistical distributions of the white Gaussian noise and the difference between the interpolated image and the original image is similar to each other. The proposed algorithm is composed of three steps. In first, the interpolated image is derived by random image interpolation. In second, we derive weighting functions that are used to apply non-local mean filtering. In the final step, the prediction error is corrected by performing non-local mean filtering by applying the selected weighting function. It can be considered as a post-processing algorithm to further reduce the prediction error after applying an arbitrary image interpolation algorithm. Simulation results show that the proposed method yields reasonable performance.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.308-318
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• 2022

Noise is a serious issue. While sending images via electronic communication, Impulse noise, which is created by unsteady voltage, is one of the most common noises in digital communication. During the acquisition process, pictures were collected. It is possible to obtain accurate diagnosis images by removing these noises without affecting the edges and tiny features. The New Average High Noise Density Median Filter. (HNDMF) was proposed in this paper, and it operates in two steps for each pixel. Filter can decide whether the test pixels is degraded by SPN. In the first stage, a detector identifies corrupted pixels, in the second stage, an algorithm replaced by noise free processed pixel, the New average suggested Filter produced for this window. The paper examines the performance of Gaussian Filter (GF), Adaptive Median Filter (AMF), and PHDNF. In this paper the comparison of known image denoising is discussed and a new decision based weighted median filter used to remove impulse noise. Using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Structure Similarity Index Method (SSIM) metrics, the paper examines the performance of Gaussian Filter (GF), Adaptive Median Filter (AMF), and PHDNF. A detailed simulation process is performed to ensure the betterment of the presented model on the Mini-MIAS dataset. The obtained experimental values stated that the HNDMF model has reached to a better performance with the maximum picture quality. images affected by various amounts of pretend salt and paper noise, as well as speckle noise, are calculated and provided as experimental results. According to quality metrics, the HNDMF Method produces a superior result than the existing filter method. Accurately detect and replace salt and pepper noise pixel values with mean and median value in images. The proposed method is to improve the median filter with a significant change.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2239-2246
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• 2011

In image signal processing, image signal is corrupted by various noises and caused the degradation phenomenon. And Images often corrupted by AWGN(additive white gaussian noise) and impulse noise which called mixed noise. In this paper, the algorithm is proposed to remove mixed noise while keeping edge information. The proposed algorithm first classifies the noise type, if the classify result is AWGN, then the mean of the output after using self-adaptive weighted mean filter and median value will be the outfiltering value. And if the noise type is impulse noise, then the noise is removed by a modified nonlinear filter. Also we compare existing methods through the simulation and using PSNR(peak signal to noise ratio) as the standard of judgement of improvement effect. The result of computer simulation on test images indicates that the proposed method is superior to traditional filtering algorithms.

• Journal of IKEEE
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• v.23 no.4
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• pp.1415-1422
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• 2019

Background subtraction is the major task of computer vision and image processing to detect changes in video. The best performing background subtraction is computationally expensive that cannot be used in real time in a typical computing environment. The proposed algorithm improves the background subtraction algorithm of the widely used MOG with the image resizing algorithm. The proposed image resizing algorithm is designed to drastically reduce the amount of computation and to utilize local information, which is robust against noise such as camera movement. Experimental results of the proposed algorithm have a classification capability that is close to the state of the art background subtraction method and the processing speed is more than 10 times faster.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.484-486
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• 2018

Recently, with the increase in use of digital equipment in various fields, the importance of image and signal processing is increasing. However, many types of noise are generated during transmission and reception of digital signal, causing errors. For this reason, noise removal is mandatorily performed during pre-processing phase in many fields. In the present paper, noise is classified through noise evaluation, and noise removal is performed to remove impulse noise and noise with AWGN-added noise. And, proposed is an algorithm which utilizes modified Gaussian filter and directional effective pixels according to noise type. Simulation results show superior noise-removal characteristics, and for objective evaluation, compared with conventional methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.933-935
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• 2015

Currently, image processing is being utilized in various fields and many studies on the image restoration is being progressed in order to eliminate the noise being generated during the process of transmitting, processing and storing of image. There are various types of noises included in the image according to the causes and types but AWGN is the most representative. In this paper, an algorithm was proposed which applies the variables differently according to the differences in surrounding pixels and central pixels within the local mask in order to mitigate the AWGN included in the image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4567-4587
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• 2017

With the growth of the Internet and the extensive applications of image editing software, it has become easier to manipulate digital images without leaving obvious traces. Copy-move is one of the most common techniques for image forgery. Image blind forensics is an effective technique for detecting tampered images. This paper proposes an improved copy-move forgery detection method based on the discrete cosine transform (DCT). The quantized DCT coefficients, which are feature representations of image blocks, are truncated using a truncation factor to reduce the feature dimensions. A method for judging whether two image blocks are similar is proposed to improve the accuracy of similarity judgments. The main transfer vectors whose frequencies exceed a threshold are found to locate the copied and pasted regions in forged images. Several experiments are conducted to test the practicability of the proposed algorithm using images from copy-move databases and to evaluate its robustness against post-processing methods such as additive white Gaussian noise (AWGN), Gaussian blurring, and JPEG compression. The results of experiments show that the proposed scheme effectively detects both copied region and pasted region of forged images and that it is robust to the post-processing methods mentioned above.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.1064-1070
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• 2012

Nowadays, image processing has been applied in a variety of fields. In order to preserve the high quality of visual the degradation phenomenon for images should be removed. Noise is one of the representative elements cause of the degradation phenomenon and AWGN(additive white Gaussian noise) always damages images. In this paper, an mixed filter algorithm, which is based on parallel denoising method, is proposed to suppress AWGN. This algorithm parallels the spatial domain wiener filter and the wavelet domain thresholding method which thresholding function is selected based on scale level. The proposed modified thresholding function which considers the dependency between parent and child coefficient performs well on suppressing noise.