• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.159-162
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• 2008

This paper presents a modified mean curvature diffusion filter to smooth speckle noise in images. Mean curvature diffusion filter has already shown good results in reducing noise in images while preserving fine details. In the mean curvature diffusion, the rate of smoothing is controlled by the local value of the diffusion coefficient chosen to be a function of the local image gradient magnitude. In this paper, the diffusion coefficient is modified to be controlled adaptively by local image surface slope and heterogeneity. The local surface slope contributes to preserving details (e.g.edges) in image and the local surface heterogeneity helps the smoothing filter consider the amount of noise in both edge and non-edge area. The proposed filter's performance is demonstrated by quantitative experiments using speckle noised aerial image and TerraSAR-X satellite image.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.11 no.2
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• pp.33-42
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• 2003

In this paper, we propose an enhancement of the ultrasonic image for non-destructive inspection of aircraft composite materials. The ultrasonic images are corrupted by a speckle noise which has the characteristic of granular pattern noise and is in all types of coherent imaging systems, the signal independent and multiplicative noise. In this paper, we derive a filter, called the AWLF(Adaptive Window Log Filter), from the nature of the speckle. The filter is made of the MEAN Filter in the edge region and Log Filter in the flat or noise region. To make a distinction between edge and flat region, we calculate the inclination around the local window instead of computing the local statistics of pixels such as local mean ${\bar{M}}$ and standard deviation ${\sigma}_s$. According to the obtained region, edge region is performed by the mean filter and flat region by the Log filter. Performance of the proposed filter is evaluated by the Enhanced Factor$(F_e)$ and the Speckle Index(SI).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9C
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• pp.555-563
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• 2011

In this paper, we propose an object based 2D image to 3D conversion algorithm by using motion estimation, color labeling and non-local mean filtering methods. In the proposed algorithm, we first extract the motion vector of each object by estimating the motion between frames and then segment a given image frame with color labeling method. Then, combining the results of motion estimation and color labeling, we extract object regions and assign an exact depth value to each object to generate the right image. While generating the right image, occlusion regions occur but they are effectively recovered by using non-local mean filter. Through the experimental results, it is shown that the proposed algorithm performs much better than conventional conversion scheme by removing the eye fatigue effectively.

• KIISE Transactions on Computing Practices
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• v.20 no.11
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• pp.573-578
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• 2014

In this letter, we propose a noise reduction method for use in magnetic resonance images that is based on non-local mean and guided image filters. Our method consists of two phases. In the first phase, the guidance image is obtained from a noisy image by using an adaptive non-local mean filter. The spread of the kernel is adaptively by controlled by implementing the concept of edgeness. In the second phase, the noisy images and the guidance images are provided to the guided image filter as input in order to produce a noise-free image. The improved performance of the proposed method is investigated by conducting experiments on standard datasets that contain magnetic resonance images. The results show that the proposed scheme is superior over the existing approaches.

• Progress in Superconductivity
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• v.5 no.1
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• pp.26-30
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• 2003

Magneto-cardiogram(MCG) signals may be highly distorted by the environmental noise, such as power-line interference, broadband white noise, surrounding magnetic noise, and baseline wondering. Several kinds of digital filters and noise cancellation methods have been designed and realized by many researchers, but these methods gave some problems that the original signal may be distorted by digital filter due to the wideband characteristics of background noise. To eliminate noise effectively without distortion of MCG signals, we performed multi-level frequency decomposition using wavelet packets and local adaptive noise cancellation in each local frequency range. In addition to the proposed wavelet filter to eliminate these various non-stationary noise elements, the local adaptive filter using the least mean square(LMS) algorithm and the soft threshold do-noising method are introduced in this paper. The signal to noise ratio(SNR) and the reconstruction square error(RSE) are calculated to evaluate the performance of the proposed method and compared with the results of the conventional wavelet filter and adaptive filter. The experimental results show that the proposed local adaptive filtering method is better than the conventional methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.1
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• pp.25-35
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• 2022

The SAP(salt-and-pepper) noise changes the pixel value to the maximum and minimum values of the dynamic region of the pixel. For this reason, unlike white Gaussian noise, SAP noise can predict the ratio of noise relatively easily. Because the condition of the neighboring pixels that can be referenced changes according to the noise ratio, it is necessary to apply different noise reduction methods according to the noise ratio. This paper proposes an adaptive switching filtering algorithm which can eliminates the SAP noise. It consists of two phases. It first detects the location of the SAP noise and calculates the noise ratio. After that, the image is reconstructed using different methods depending on which of the three sections the calculated noise ratio belongs to. As a result of the experiment, the proposed method showed superior objective and subjective image quality compared to the previous methods such as MF, AFSWMF, NAMF and RWMF.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.9
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• pp.798-809
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• 2013

In this paper, we propose robust relay and destination filter design methods for the multi-user peer-to-peer amplify-and-forward relaying systems while taking imperfect channel knowledge into consideration. Specifically, the relay and destination filter sets are developed to minimize the sum mean-squared-error (MSE). We first present a robust joint optimum relay and destination filter calculation method with an iterative algorithm. Motivated by the need to reduce computational complexity of the iterative scheme, we then formulate a simplified sum MSE minimization problem using the relay filter decomposability, which lead to two robust sub-optimum non-iterative design methods. Finally, we propose robust modified destination filter design methods which require only local channel state information between relay node and a specific destination node. The analysis and simulation results verify that, compared with the optimum iterative method, the proposed non-iterative schemes suffer a marginal loss in performance while enjoying significantly improved implementation efficiencies. Also it is confirmed that the proposed robust filter design methods provide desired robustness in the presence of channel uncertainty.

• Journal of Broadcast Engineering
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• v.20 no.1
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• pp.153-163
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• 2015

This paper proposes a rain detection and removal algorithm that is robust against camera motion in video sequences. In detection part, the proposed algorithm initially detects possible rain streaks by using intensity properties and spatial properties. Then, the rain streak candidates are selected based on Gaussian distribution model. In removal part, a non-rain block matching algorithm is performed between adjacent frames to find similar blocks to the block that has rain pixels. If the similar blocks to the block are obtained, the rain region of the block is reconstructed by non-local means (NLM) filter using the similar neighbors. Experimental results show that the proposed algorithm outperforms the previous works in terms of subjective visual quality of de-rained video sequences.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.11
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• pp.1523-1534
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• 2000

The performance of a number of existing dynamic subgrid-scale(SGS) models is evaluated in large-eddy simulations(LES) of two prototype transitional and turbulent shear flows, a planar jet and a channel flow. The dynamic SGS models applied include the dynamic Smagorinsky model(DSM);Germano et al. 1991, Lully 1992), the dynamic tow-component model(DTM; Akhavan et al. 2000), the dynamic mixed model(DMM;Zang et al, 1993). and the dynamic two-parameter model(DTPM; Salvetti & Banerjee 1995). The results are compared with those for DNS for their evaluation. The LES results demonstrate the superior performance of DTM with use of a sharp cutoff filter and DMM with use of a box filter, as compared to their respect counterpart DSM, in predicting the mean statistics, spectra and large-scale structure of the flow, Such features of DTM and DMM derive from the construction of the models in which tow separate terms are included to represent the SGS interactions; a Smagorinsky edd-viscosity term to account for the non-local interactions, and a local-interaction term to account for the nonlinear dynamics between the resolved and subgrid scales in the vicinity of the LES cutoff. As well, overall the SGS models using a sharp cutoff filter are more successful than those using a box filter in capturing the statistics and structure of the flow. Finally, DTPM is found to be compatible or inferior to DMM.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2197-2204
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• 2015

In recent years, medical image diagnosis has growing significant momentous in the medicinal field. Brain and lung image of patient are distorted with salt and pepper noise is caused by moving the head and chest during scanning process of patients. Reconstruction of these images is a most significant field of diagnostic evaluation and is produced clearly through techniques such as linear or non-linear filtering. However, restored images are produced with smaller amount of noise reduction in the presence of huge magnitude of salt and pepper noises. To eliminate the high density of salt and pepper noises from the reproduction of images, a new efficient fuzzy based median filtering algorithm with a moderate elapsed time is proposed in this paper. Reproduction image results show enhanced performance for the proposed algorithm over other available noise reduction filtering techniques in terms of peak signal -to -noise ratio (PSNR), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), image enhancement factor (IMF) and structural similarity (SSIM) value when tested on different medical images like magnetic resonance imaging (MRI) and computer tomography (CT) scan brain image and CT scan lung image. The introduced algorithm is switching filter that recognize the noise pixels and then corrects them by using median filter with fuzzy two-sided π- membership function for extracting the local information.