• Journal of information and communication convergence engineering
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• v.10 no.1
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• pp.45-52
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• 2012

Thedenoising of a natural image corrupted by Gaussian noise is a long established problem in signal or image processing. Today the research is focus on the wavelet domain, especially using the wavelet threshold method. In this paper, a waveletbased image denoising modified adaptive thresholding method is proposed. The proposed method computes thethreshold adaptively based on the scale level and adaptively estimates wavelet coefficients by using a modified thresholding function that considers the dependency between the parent coefficient and child coefficient and the soft thresholding function at different scales. Experimental results show that the proposed method provides high peak signal-to-noise ratio results and preserves the detailed information of the original image well, resulting in a superior quality image.

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

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.127-129
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• 2012

Due to the prevalence of digital camera, multi-media etc. the image is being used in everyday life. However, noise always damages the image and the image denoising technology is important part for improving the image visual quality. There are many existing methods to remove noise such as wiener filter, mean filter and VisuShrink etc. However, they perform not good enough for denoising. Hence, in this paper we proposed a hybrid filter algorithm which consists of wiener filter and modified wavelet based thresholding method using adaptive threshold and thresholding function. The proposed algorithm shows not only better low frequency and high frequency property, but also the outstanding noise suppression and edge preservation properties.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8C
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• pp.788-794
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• 2003

It is very important to construct statistical model in order to exactly estimate the signal variance from the noisy image. By using estimated variance of original image, in general, Wiener filter is constructed, and it is applied to the noisy image. In this paper, we propose a new statistical mixture modeling of wavelet coefficients for image denoising. Firstly, a simple classification method is used to construct a significance map that captures significant property of wavelet coefficients. Based upon the significance map, the state probabilities of mixture model is computed, and signal variance is estimated by using them. Experimental results show that the proposed method yields 0.1-0.2㏈ higher PSNR than conventional methods for image denoising.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.18-20
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• 2006

The statistics for the neighbor differences between the particular pixels and their neighbors are introduced. They are incorporated into the filter to remove additive Gaussian noise contaminating images. The derived denoising method corresponds to the maximum likelihood estimator for the heavy-tailed Gaussian distribution. The error norm corresponding to our estimator from the robust statistics is equivalent to Huber's minimax norm. Our estimator is also optimal in the respect of maximizing the efficacy under the above noise environment.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.5
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• pp.601-608
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• 2014

In this study we propose an image denoising algorithm appropriate for mobile smart phone equipped with limited computing ability, which has better performance and at the same time comparable quality comparing with previous studies. The proposed image denoising algorithm for mobile smart phone cameras in low level light environment reduces computational complexity and also prevents edge smoothing by extracting just Gaussian noises from the noisy input image. According to the experiment result, we verified that our algorithm has much better PSNR value than methods applying mean filter or median filter. Also the result image from our algorithm has better clear quality since it preserves edges while smoothing input image. Moreover, the suggested algorithm reduces computational complexity about 52% compared to the method applying original Laplacian mask computation, and we verified that our algorithm has good denoising quality by implementing the algorithm in Android smart phone.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1773-1778
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• 2012

Nowadays, the high quality of image is required with the demand for digital image processing devices is rapidly increasing. But image always damaged by many kinds of noises and it is necessary to remove noise and the denoising becomes one of the most important fields. In many cases image is corrupted by AWGN(additive white Gaussian noise). In this paper, we proposed an improved denoising algorithm with edge preservation. The proposed algorithm averages values processed by spatial weighted filter and self adaptive weighted filter. Then we add the value which is computed by the equation considering variance of mask and the estimated noise variance. Through the experience, the proposed filter performs well on noise suppression and edge preservation properties and improves the image visual quality.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.47-54
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• 2005

In general, wavelet coefficients are composed of a few large coefficients and a lot of small coefficients. In this paper, we propose image denoising algorithm using Bernoulli-Gaussian mixture model based on sparse characteristic of wavelet coefficient. The Bernoulli-Gaussian mixture is composed of the multiplication of Bernoulli random variable and Gaussian mixture random variable. The image denoising is performed by using Bayesian estimation. We present an effective denoising method through simplified parameter estimation for Bernoulli random variable using local expected squared error. Simulation results show our method outperforms the states-of-art denoising methods when using orthogonal wavelets.

• Journal of IKEEE
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• v.25 no.3
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• pp.544-547
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• 2021

Recently, various display products are being applied to automobiles. In the process of acquiring an image from a display product, a large amount of additive white Gaussian noise(AWGN) is generated. Generally known denoising techniques focus on removing noise, so detailed components including image information are proportionally lost in the process of removing noise. The algorithm proposed in this paper proposes a method to effectively remove noise while preserving the detail of image information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1886-1891
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• 2017

Denoising which is one of major research topics in the image processing deals with recovering the noisy images. Natural images are well known not only for their local but also nonlocal similarity. Patterns of unique edges and texture which are crucial for understanding the image are repeated over the nonlocal region. In this paper, a nonlocal similarity based denoising algorithm is proposed. First for every blocks of the noisy image, nonlocal similar blocks are gathered to construct a overcomplete data set which are inherently sparse in the transform domain due to the characteristics of the images. Then, the sparse transform coefficients are filtered to suppress the non-sparse additive noise. Finally, the image is recovered by aggregating the overcomplete estimates of each pixel. Performance experiments with several images show that the proposed algorithm outperforms the conventional methods in removing the additive Gaussian noise effectively while preserving the image details.