• Journal of Information Processing Systems
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• v.14 no.2
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• pp.539-551
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• 2018

Images are unavoidably contaminated with different types of noise during the processes of image acquisition and transmission. The main forms of noise are impulse noise (is also called salt and pepper noise) and Gaussian noise. In this paper, an effective method of removing mixed noise from images is proposed. In general, different types of denoising methods are designed for different types of noise; for example, the median filter displays good performance in removing impulse noise, and the wavelet denoising algorithm displays good performance in removing Gaussian noise. However, images are affected by more than one type of noise in many cases. To reduce both impulse noise and Gaussian noise, this paper proposes a denoising method that combines adaptive median filtering (AMF) based on impulse noise detection with the wavelet threshold denoising method based on a Gaussian mixture model (GMM). The simulation results show that the proposed method achieves much better denoising performance than the median filter or the wavelet denoising method for images contaminated with mixed noise.

• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.591-596
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• 2011

Image denoising is a key issue in all image processing researches. Generally, the quality of an image could be corrupted by a lot of noise due to the undesired conditions of image acquisition phase or during the transmission. Many approaches to image restoration are aimed at removing either Gaussian or impulse noise. Nevertheless, it is possible to find them operating on the same image, which is called mixed noise and it produces a hard damage. In this paper, we proposed noise type classification method and a mixed nonlinear filter for mixed noise suppression. The proposed filtering scheme applies a modified adaptive switching median filter to impulse noise suppression and an efficient nonlinear filer was carried out to remove Gaussian noise. The simulation results based on Matlab show that the proposed method can remove mixed Gaussian and impulse noise efficiently and it can preserve the integrity of edge and keep the detailed information.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.54-60
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• 2021

Various digital devices were supplied throughout the Fourth Industrial Revolution. Accordingly, the importance of data processing has increased. Data processing significantly affects equipment reliability. Thus, the importance of data processing has increased, and various studies have been conducted on this topic. This study proposes a modified Gaussian filter algorithm based on a fuzzy membership function. The proposed algorithm calculates the Gaussian filter weight considering the standard deviation of the filtering mask and computes an estimate according to the fuzzy membership function. The final output is calculated by adding or subtracting the Gaussian filter output and estimate. To evaluate the proposed algorithm, simulations were conducted using existing additive white Gaussian noise removal algorithms. The proposed algorithm was then analyzed by comparing the peak signal-to-noise ratio and differential image. The simulation results show that the proposed algorithm has superior noise reduction performance and improved performance compared to the existing method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.379-381
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• 2011

The image signal is often affected by the existence of noise, noise can occur during image capture, transmission or processing phases. noises caused the degradation phenomenon and demage the original signal information. Many studies are being accomplished to restore those signals which corrupted by mixed noise. In this paper, we proposed mixed weighted filter for removing Gaussian and impulse noise. we first charge the noise type, then, Gaussian is removed by a weighted mean filter and impulse noise is removed by self-adaptive weighted median filter that can not only remove mixed noise but also preserve the details. And through the simulation, we compared with the conventional algorithms and indicated that proposed method significant improvement over many other existing algorithms and can preserve image details efficiently.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.73-76
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• 2018

A Gaussian noise is caused by surrounding environment or channel interference when transmitting image. The noise reduces not only image quality degradation but also high-level image processing performance. The Non-Local Means (NLM) filter finds similarity in the neighboring sets of pixels to remove noise and assigns weights according to similarity. The weighted average is calculated based on the weight. The NLM filter method shows low noise cancellation performance and high complexity in the process of finding the similarity using weight allocation and neighbor set. In order to solve these problems, we propose an algorithm that shows an excellent noise reduction performance by using Summed Square Image (SSI) to reduce the complexity and applying the weighting function based on a cosine Gaussian kernel function. Experimental results demonstrate the effectiveness of the proposed algorithm.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.32-35
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• 2005

Image denoising is basic work for image processing, analysis and computer vision. This paper proposes a novel algorithm based on wavelet threshold for image denoising, which is combined with the linear CLS (Constrained Least Squares) filtering and thresholding methods in the transform domain. We demonstrated through simulations with images contaminated by white Gaussian noise that our scheme exhibits better performance in both PSNR (Peak Signal-to-Noise Ratio) and visual effect.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.675-681
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• 2019

Noise removal is a pre-requisite procedure in image processing, and various methods have been studied depending on the type of noise and the environment of the image. However, for image processing with high-frequency components, conventional additive white Gaussian noise (AWGN) removal techniques are rather lacking in performance because of the blurring phenomenon induced thereby. In this paper, we propose an algorithm to minimize the blurring in AWGN removal processes. The proposed algorithm sets the high-frequency and the low-frequency component filters, respectively, depending on the pixel properties in the mask, consequently calculating the output of each filter with the addition or subtraction of the input image to the reference. The final output image is obtained by adding the weighted data calculated using the standard deviations and the Gaussian distribution with the output of the two filters. The proposed algorithm shows improved AWGN removal performance compared to the existing method, which was verified by simulation.

• Journal of the Korean Vacuum Society
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• v.8 no.3B
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• pp.315-321
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• 1999

In this paper, the SEM image processing system based on PC is designed, and a new noise reduction filtering algorithm is proposed. The SEM image obtained in semiconductor processing line is sensitive to noise, the weighted-D filter can remove uniform and Gaussian noise effectively, but can not remove impulse noise properly, A new improved filtering algorithm is proposed to reduce mixed-noise. The performance of the proposed filter is quantitatively evaluated by use of the normalized mean square errors (NMSE). The experimental results show that the performance of the proposed filter is obtained between 0.96 and 2.5 times better than that of weighted-D filter in NMSE evaluation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.8
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• pp.2001-2007
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• 2014

Image signal related technology has been developing via various display equipment development and popularization of contents. However, errors occur in these image contents due to addition of excess noise from several cause during the process of general image signal data processing, transmission and storage. In terms of noise added to the image content, there are various types in accordance with cause of occurrence and form, and it is typically impulse noise, gaussian noise and complex noise which is composed of two types of overlapping noise. In this paper, complex algorithm is suggested in order to lessen the effect of mixed noise added to the image content by putting it through noise judgement process and categorizing each into impulse and gaussian noise and processing them separately. And in order to demonstrate the superiority of the suggested algorithm, PSIN(peak signal to noise ratio) was used as the standard of judgement.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.455-458
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• 2008

Former times Information Technology generally has only depended on text or sound, while nowadays information is being moved through a variety of image media. Cell phone, TV and computer have been major elements of modem society as mediators using image signal. Therefore, image signal processing also has been treated importantly and done actively. The processing has been developed in many fields of digital image processing technologies as image data compression, recognition, restoration, etc. Noises are inevitably generated by using the signals during the processing, and typical types of the noise are Impulse(salt & pepper) and AWGN(Addiction White Gaussian Noise). To reduce the noise, various kinds of filters have been developed, and according to each noise, it is being used different filter each. However, the noise is not generated by one signal but by a complex. In this paper, I suggested an image filter to remove the complex noise, and compared with existing filters' methods for verification.