• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1176-1182
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• 2021

Recently, with the development of artificial intelligence and IoT technology, automation, and unmanned work are progressing in various fields, and the importance of image processing, which is the basis of AI object recognition, is increasing. In particular, in systems that require detailed data processing, noise removal is used as a preprocessing step, but the existing algorithm does not consider the noise level of the image, so it has the disadvantage of blurring in the filtering process. Therefore, in this paper, we propose a modified Gaussian filter that determines the weight by determining the noise level of the image. The proposed algorithm obtains the noise estimate for the AWGN of the image using quadtree segmentation, determines the Gaussian weight and the pixel weight, and obtains the final output by convolution with the local mask. To evaluate the proposed algorithm, it was simulated compared to the existing method, and superior performance was confirmed compared to the existing method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.2
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• pp.57-68
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• 2023

Because salt and pepper noise is a type of impulse, even a small amount of noise could cause a large image degradation. In this paper, we proposed a salt-and-pepper noise removal method using the convolutional neural network. It consists of four phases. In the first step, the proposed method reconstructs noisy image using a traditional salt-and-pepper noise reduction method, and in the second step, the result image of previous step is filtered with Gaussian low pass filter. After that, we reconstruct the filtered image using convolution neural network. In the last step, the pixels with salt-and-pepper noise are replaced with the result of previous phase. Simulation results show that the proposed method yields not only objective image qualities(PSNR, SSIM) but also subjective image qualities for all SAP noise ratios.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.398-401
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• 1997

The EMG signal of spinal cord injured patient is very feeble because that the information from central nervous system is not sufficiently transmitted to molter neuron or muscle fiber. Therefore the observer can not observe contraction and relaxation movement of muscle from the raw EMG signal. In this paper, we propose the muscle contraction and relaxation pattern analysis method of spinal cord injured patient whose EMG signal is composed of the sum of motor unit action potential train with additive white Gaussian noise and impulsive noise. From the EMG model, we denoise impulsive noise using median filter which is a kind of nonlinear filter and the output of median filter is transformed to wavelet transform domain for denoising additive white Gaussian noise using threshold level removal technique. As a result, we can obtain the clear contraction and relaxation pattern.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2701-2706
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• 2009

There are Impulse noise and AWGN in a general image processing. Various methods have been proposed to remove these noises. Well-known filters are Mean, Min-max and Median filter and these show different characteristics depending on the noises. When Impulse noise and AWGN are in superposition environment, single filter doesn't remove noises well. Therefore in this paper, we suggested a switching filter using a probability of noise to restore images in this environment. And we compared a filter with conventional method through simulations.

• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.455-464
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• 2007

Lee(2007) suggested the Point-Jacobian iteration MAP estimation(PJIMAP) for noise removal of the images that are corrupted by multiplicative speckle noise. It is to find a MAP estimation of noisy-free imagery based on a Bayesian model using the lognormal distribution for image intensity and an MRF for image texture. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. The MRF is incorporated into digital image analysis by viewing pixel types as states of molecules in a lattice-like physical system. In this study, the MAP estimation is computed by the Point-Jacobian iteration using adaptive parameters. At each iteration, the parameters related to the Bayesian model are adaptively estimated using the updated information. The results of the proposed scheme were compared to them of PJIMAP with SAR simulation data generated by the Monte Carlo method. The experiments demonstrated an improvement in relaxing speckle noise and estimating noise-free intensity by using the adaptive parameters for the Ponit-Jacobian iteration.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.1001-1004
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• 2006

Many researches have been processed to reconstruct corrupted an image by noise in fields of signal processing such as image recognition and compute. vision, and AWGN(additive white gaussian noise) and impulse noise are representative. Impulse noise consists of fired-valued(salt & pepper) impulse noise and random-valued impulse noise, and non-linear filters such as SM(standard median) filters are used to remove this noise. But basic SM filters still generate many errors in edge regions of an image, and in order to overcome this problem a variety of methods have been researched. In this paper, we proposed an impulse noise removal algorithm which is superior to the edge preserving capacity. At this tine, after detecting a noise by using the noise detector, we applied a noise removal algorithm based on the min-max operation and compared the capacity with existing methods through simulation.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.270-273
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• 2007

In this paper, a SAR-despeck1ing approach of adaptive iteration based a Bayesian model using the lognormal distribution for image intensity and a Gibbs random field (GRF) for image texture is proposed for noise removal of the images that are corrupted by multiplicative speckle noise. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. The MRF is incorporated into digital image analysis by viewing pixel types as states of molecules in a lattice-like physical system. The iterative approach based on MRF is very effective for the inner areas of regions in the observed scene, but may result in yielding false reconstruction around the boundaries due to using wrong information of adjacent regions with different characteristics. The proposed method suggests an adaptive approach using variable parameters depending on the location of reconstructed area, that is, how near to the boundary. The proximity of boundary is estimated by the statistics based on edge value, standard deviation, entropy, and the 4th moment of intensity distribution.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.125-131
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• 2019

Through the 4th Industrial Revolution, various digital equipments are being distributed, and accordingly, the importance of data processing is increasing. As data processing has a great effect on the reliability of equipment, its importance is increasing, and various studies are being conducted. In this paper, we propose an algorithm to remove AWGN in consideration of the noise in the image. The proposed algorithm is used in the filtering process by inferring the standard deviation of the image noise. The noise is removed by dividing the filter for the high frequency component and the filter for the low frequency component compared with the standard deviation of the filtering mask. The proposed algorithm is simulated with the existing methods for evaluation and compared and analyzed by difference image, PSNR and profile. The proposed algorithm minimizes the effect of noise and preserves the important characteristics of the image and shows the performance of efficient noise removal.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.790-792
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• 2017

As the demand of digital image processing devices has been rapidly increased recently, the excellent image quality is required. However, degradation can be occurred with multiple causes during transmission and processing process. Therefore, the needs to eliminate the noise are increased and the noise elimination technology became the major study area. Therefore, image restoration algorithm was suggested to apply the filter differently by edge and non-edge areas, using modified edge detection with preprocessing process so as to relieve the effect of additive white Gaussian noise(AWGN) which is added in the image, in this article. In addition, it was compared with the existing methods using peak signal to noise ratio(PSNR) as the objective determination standard of the improvement effect.