• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.17-23
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• 2010

In this paper, we present an adaptive noise removal method using local statistics and generalized Gaussian filter. we propose a generalized Gaussian filter for removing noise effectively and detecting noise adaptively using local statistics based human visual system. The simulation results show the objective and subjective capabilities of the proposed algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.137-140
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• 2021

본 논문에서는 X-Ray 영상에서 발 뼈의 골절 영역을 분석 및 진단하기 위한 전단계로서 X-Ray 영상에서 뼈와 피부 영역을 분할하는 방법을 제안한다. 제안된 방법은 X-Ray 영상의 피부 영역과 발 뼈 영역을 분할하기 위해 가우시안 필터를 적용하여 DOG 영상을 생성한다. 그러나 기존의 가우시안 필터는 정적으로 적용되기 때문에 영상을 촬영하는 부위와 각도에 따라 영상의 특성이 달라지는 X-Ray 영상에 적용하기에 부적합하다. 따라서 부위와 각도에 따라 영상의 특성 변화에 민감하지 않는 동적 가우시안 필터를 제안한다. 실험 결과에서는 제안하는 동적 가우시안 필터와 기존의 정적인 가우시안 필터를 각각 적용하여 생성된 DOG 영상에 대해서 발 뼈 영역과 피부 영역을 분할하고, 효율성을 TPR과 특이도로 분석한 결과, 제안된 동적 가우시안 필터를 적용한 방법이 정적 가우시안 필터보다 평균적으로 TPR는 0.12%와 특이도는 평균적으로 0.36%가 개선된 것을 확인하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9C
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• pp.863-870
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• 2009

Bilateral filter (BF) is a nonlinear filter for sharpness enhancement and noise removal. The BF performs the function by the two Gaussian filters, the domain filter and the range filter. To apply the BF to infrared (IR) small target detection, the standard deviation of the two Gaussian filters need to be changed adaptively between the background region and the target region. This paper presents a new BF with the adaptive standard deviation based on the analysis of the edge component of the local window, also having the variable filter size. This enables the BF to perform better and become more suitable in the field of small target detection Experimental results demonstrate that the proposed method is robust and efficient than the conventional methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.623-625
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• 2012

The restoration of an image corrupted by Gaussian noise is an important task in image processing. There are many kinds of filters are proposed to remove Gaussian noise such as Gaussian filter, mean filter, weighted filter, etc. However, they perform not good enough for denoising and edge preservation. Hence, in this paper we proposed an adaptive weighted filter which considers spatial distance and the estimated variance of noise. We also compared the proposed method with existing methods through the simulation and used MSE(mean squared error) as the standard of judgement of improvement effect.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.3
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• pp.629-635
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• 2017

In this paper, we proposed a nonlinear synthesis filter for noise reduction to reduce the effects of Gaussian noise and impulse noise. When the centralization of the local mask is judged to be Gaussian noise by the noise judgment, the weight value of the weight filter are applied differently according to the spatial weight filter and the pixel change by using the sample variance in the local mask. And if it is determined as the impulse noise, we proposed an algorithm that applies different weights of local histogram weight filter and standard median filter according to noise density of mask. In order to evaluate the performance of the proposed filter algorithm, we used PSNR(peak signal to noise ratio) and compared existing methods and proposed filter algorithm in the mixed noise environment with Gaussian noise, impulsive noise, and two noises mixed.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.232-238
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• 2013

This paper study the position estimation of MBK system according to the non-linear filter for non-Gaussian noise in underwater sensor networks. In the filter to estimate location, recently, the extended Kalman filter (EKF) and particle filter are getting attention. EKF is widely used due to the best algorithm in the Gaussian noise environment, but has many restrictions on the usage in non-Gaussian noise environment such as in underwater. In this paper, we propose the improved One-Dimension Particle Filter (ODPF) using the distribution re-interpretation techniques based on the maximum likelihood. Through the simulation, we compared and analyzed the proposed particle filter with the EKF in non-Gaussian underwater sensor networks. In the case of both the sufficient statistical sample and the sufficient calculation capacity, we confirm that the ODPF's result shows more accurate localization than EKF's result.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.273-285
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• 2007

Most stereo matching methods use intensity values in small image patches to measure the correspondence between two points. If the noisy pixels are used in computing the corresponding point, the matching performance becomes low. For this reason, the noise plays a critical role in determining the matching performance. In this paper, we propose a method for combining intensity and edge filters robust to the noise in order to improve the performance of stereo matching using high resolution satellite imagery. We used intensity filters such as Mean, Median, Midpoint and Gaussian filter and edge filters such as Gradient, Roberts, Prewitt, Sobel and Laplacian filter. To evaluate the performance of intensity and edge filters, experiments were carried out on both synthetic images and satellite images with uniform or gaussian noise. Then each filter was ranked based on its performance. Among the intensity and edge filters, Median and Sobel filter showed best performance while Midpoint and Laplacian filter showed worst result. We used Ikonos satellite stereo imagery in the experiments and the matching method using Median and Sobel filter showed better matching results than other filter combinations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2261-2268
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• 2010

The information captured by imaging devices such as CCD or CIS may contain external noises through the processes of passing signals or storing images. In this paper, we propose a Modified Sigma Filter (MSF) algorithm to reduce such noises. In experiment, we verified that our MSF algorithm showed better performance in PSNR and 1D plot of simulation results compared with Gaussian Filter (GF), Local Sigma Filter (LSF). Tested images include random Gaussian Noises.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.2
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• pp.192-197
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• 2017

In this paper, image filter optimization method based on common sub-expression elimination is proposed for low-power image feature extraction hardware design. Low power and high performance object recognition hardware is essential for industrial robot which is used for factory automation. However, low area Gaussian gradient filter hardware design is required for object recognition hardware. For the hardware complexity reduction, we adopt the symmetric characteristic of the filter coefficients using the transposed form FIR filter hardware architecture. The proposed hardware architecture can be implemented without degradation of the edge detection data quality since the proposed hardware is implemented with original Gaussian gradient filtering algorithm. The expremental result shows the 50% of multiplier savings compared with previous work.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.4
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• pp.274-282
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• 2019

Vision-based navigation of unmaned aerial vehicle is a significant technology that can reinforce the vulnerability of the widely used GPS/INS integrated navigation system. However, the existing image matching algorithms are not suitable for matching the aerial image with the database. For the reason, this paper proposes particle filters using Gaussian mixture models to deal with matching between aerial image and database for vision-based navigation. The particle filters estimate the position of the aircraft by comparing the correspondences of aerial image and database under the assumption of Gaussian mixture model. Finally, Monte Carlo simulation is presented to demonstrate performance of the proposed method.