• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.1055-1063
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• 2012

The noise of image is added by many environmental factors. Therefore, we need to remove these noises using the conventional filtering methods, which are optimized based on the statistical characteristic of noise. In direct restoration method, there is an inverse filter and the wiener filter. Here, the wiener filter is the optimized filter in the view of least square method. First, we are going to study the inverse filter, wiener filter, constraint least square filter. Second, in order to control the quantity, we use the parameters instead of the power spectrum ratio. But, these parameters have the conflicting condition, therefore, we optimized the variables using parametric wiener filter which adjust the application appropriately. In the simulation results, the contrast of the degraded image was enhanced and the noise was removed. Comparative experimentation was demonstrated edge preserving and noise removing property.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.883-885
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• 2013

Image restoration refers to removing different kinds of noise added to image, and to reducing effect of noise upon image. For image restoration, some methods such as mean filter, median filter and weighted filter were proposed, but the existing methods have poor denoising and edge-reserved performance. Therefore, in this paper modified median filter algorithm was proposed that enlarges mask size according to median value of mask in order to remove noise efficiently. And, it was compared by simulation to the existing methods, and MSE(mean squared error) was used on a criterion of evaluation.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.208-213
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• 2010

Generally, the mal factor for speech recognition is the background noise in speech recognition. The noise is the reason to reduce the speech recognition performance. Owing to the fact, the place to recognize is very important. To improve the recognition performance from the sound having noise, we implemented the noise filtered Wiener filter at the signal process step which adopted the FIR filter. In FIR filter, it deal with the filtered speech signal which is appropriate frequency range of human speech frequency range. Therefore, we make the recognition system distinguish between noise and speech sound from the incoming speech signal.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2173-2179
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• 2015

In digital era image processing has been utilized in a variety of media such as TV, camera and smart phone. Typically salt and pepper noise are generated by various causes during the analysis, identification, and processing of image data. Principal filters such as SMF, CWMF, and AMF have been used to remove these noise. But the existing filters fall short of edge preservation and noise elimination in high noise densities. Thus, a processing algorithm, on which the size of deformable mask varies depending on the noise density, is proposed to remove salt and pepper noise effectively in this study. The performance of the proposed method was evaluated compared with the existing methods using PSNR.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.40-47
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• 2009

A motion adaptive video noise reduction scheme is proposed by cascading a temporal filter and a spatial filter. After a noise-robust motion detection is performed with a pre-spatial filter, the strength of the motion adaptive temporal filter is controlled by the amount of temporal movement. In order to fully utilize the temporal correlation of video signal, noisy input image is processed first by the temporal filter, therefore, image details of temporally stationary region are quite well preserved while undesired noises are suppressed. In contrast to the pre-spatial filter used for the robust motion detection, the cascaded post-spatial filter removes the remained noises by considering the strength of the temporal filter and the spatial self-similarity search results obtained from the pre-spatial filter.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.312-314
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• 2015

With progress in information technology, demand for imaging devices such as display devices is increasing. But noise occurs due to various reasons during the process of acquiring, transmitting or processing the image data. Filters used to remove salt and pepper noise include SMF, CWMF and AWMF. In areas where the noise density is high, the removal of noise is undermined. This paper suggests an adjusted median filter algorithm that transforms the noise pixels to more effectively remove salt and pepper noise.

• 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.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.233-245
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• 2008

In this work, a new adaptive weighted mean filter is proposed for preserving image details while effectively suppressing impulse noise. The proposed filter is based on a noise pixel detection-estimation strategy. All the pixels are first detected using an impulse noise detector. Then the detected noise pixels are replaced with the output of the weighted mean filter over adaptive working window according to the rate of corrupted neighborhood pixels, while noise-free pixels are left unaltered. We compare the proposed filter to other existing filters in the qualitative measure and quantitative measures such as PSNR and MAE as well as computation time to verify the capability of the proposed filter. Extensive simulations show that the proposed filter performs better than other filters in impulse noise suppression and detail preservation without increasing of running time.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.151-156
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• 2012

In this paper robust nonlinear image denoising algorithms are introduced for the distribution which is Gaussian in the center and Laplacian in the tails. The distribution is known as the least favorable ${\epsilon}$-contaminated normal distribution that maximizes the asymptotic variance. The proposed filter proves to be the maximum likelihood estimator under the heavy-tailed Gaussian noise environments. It is optimal in the respect of maximizing the efficacy under the above noise environment. Another filter for reducing impulsive noise is proposed by mixing with the myriad filter to propose an amplitude-limited myriad filter. Extensive experiment is conducted with images corrupted with ${\alpha}$-stable noise to analyze the behavior and performance of the proposed filters.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.175-182
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• 2017

Noises are usually generated by various external causes and low quality devices in image data acquisition and recording as well as by channel interference in image transmission. Since these noise signals result in the loss of information, subsequent image processing is subject to the corruption of the original image. In general, image processing is performed in the mixed noise environment where common types of noise, known to be Gaussian and impulse, are present. This study proposes an iterative weighted mean filter for reducing mixed type of noise. Impulse noise pixels are first turned off in the input image, then $3{\times}3$ sliding window regions are processed by replacing center pixel with the result of weighted mean mask operation. This filtering processes are iterated until all the impulse noise pixels are replaced. Applied to images corrupted by Gaussian noise with ${\sigma}=10$ and different levels of impulse noise, the proposed filtering method improved the PSNR by up to 12.98 dB, 1.97 dB, 1.97 dB respectively, compared to SAWF, AWMF, MMF when impulse noise desities are less than 60%.