• Title/Summary/Keyword: Gaussian filter

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Fast Bilateral Filtering Using Recursive Gaussian Filter for Tone Mapping Algorithm

  • Dewi, Primastuti;Nam, Jin-Woo;Cha, Eui-Young
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2010.05a
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    • pp.176-179
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    • 2010
  • In this paper, we propose a fast implementation of Bilateral filter for tone mapping algorithm. Bilateral filter is able to preserve detail while at the same time prevent halo-ing artifacts because of improper scale selection by ensuring image smoothed that not only depend on pixel closeness, but also similarity. We accelerate Bilateral filter by using a piecewise linear approximation and recursive Gaussian filter as its domain filter. Recursive Gaussian filter is scale independent filter that combines low cost 1D filter which makes this filter much faster than conventional convolution filter and filtering in frequency domain. The experiment results show that proposed method is simpler and faster than previous method without mortgaging the quality.

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Position Estimation of MBK system for non-Gaussian Underwater Sensor Networks (비가우시안 노이즈가 존재하는 수중 환경에서 MBK 시스템의 위치 추정)

  • Lee, Dae-Hee;Yang, Yeon-Mo;Huh, Kyung Moo
    • 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.

An Adaptive Noise Removal Method Using Local Statistics and Generalized Gaussian Filter (국부 통계 특성 및 일반화된 Gaussian 필터를 이용한 적응 노이즈 제거 방식)

  • Song, Won-Seon;Nguyen, Tuan-Anh;Hong, Min-Cheol
    • 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.

Modified Adaptive Gaussian Filter for Removal of Salt and Pepper Noise

  • Li, Zuoyong;Tang, Kezong;Cheng, Yong;Chen, Xiaobo;Zhou, Chongbo
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.8
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    • pp.2928-2947
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    • 2015
  • Adaptive Gaussian filter (AGF) is a recently developed switching filter to remove salt and pepper noise. AGF first directly identifies pixels of gray levels 0 and 255 as noise pixels, and then only restored noise pixels using a Gaussian filter with adaptive variance based on the estimated noise density. AGF usually achieves better denoising effect in comparison with other filters. However, AGF still fails to obtain good denoising effect on images with noise-free pixels of gray levels 0 and 255, due to its severe false alarm in its noise detection stage. To alleviate this issue, a modified version of AGF is proposed in this paper. Specifically, the proposed filter first performs noise detection via an image block based noise density estimation and sequential noise density guided rectification on the noise detection result of AGF. Then, a modified Gaussian filter with adaptive variance and window size is used to restore the detected noise pixels. The proposed filter has been extensively evaluated on two representative grayscale images and the Berkeley image dataset BSDS300 with 300 images. Experimental results showed that the proposed filter achieved better denoising effect over the state-of-the-art filters, especially on images with noise-free pixels of gray levels 0 and 255.

Improvement of Halo Effect Using Adaptive Gaussian Filter in Dehazing (안개제거에 적응 Gaussian Filter 를 이용한 후광효과 개선)

  • Kim, Sang-Wook;Shin, Dong-Won
    • Proceedings of the Korea Information Processing Society Conference
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    • 2011.11a
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    • pp.326-329
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    • 2011
  • 안개나 스모그 등으로 인한 영상의 왜곡에 대해 Dark Channel Prior 를 이용해 안개제거를 하면 깨끗한 결과 영상을 얻을 수 있다. 하지만 이 기법에서 전달량을 정련할 때 많은 시간이 걸리는데 계산 속도 면을 개선하기 위해 Gaussian Filter 를 사용해 정련한다. 이 때 단순한 Gaussian Filter 를 사용하게 되면 결과영상에서 후광효과가 생기게 된다. 후광효과를 줄이기 위해 본 논문에서 제안한 적응 Gaussian Filter 를 사용해 영상을 복원시킨다.

Satellite Orbit Determination using the Particle Filter

  • Kim, Young-Rok;Park, Sang-Young
    • Bulletin of the Korean Space Science Society
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    • 2011.04a
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    • pp.25.4-25.4
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    • 2011
  • Various estimation methods based on Kalman filter have been applied to the real-time satellite orbit determination. The most popular method is the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). The EKF is easy to implement and to use on orbit determination problem. However, the linearization process of the EKF can cause unstable solutions if the problem has the inaccurate reference orbit, sparse or insufficient observations. In this case, the UKF can be a good alternative because it does not contain linearization process. However, because both methods are based on Gaussian assumption, performance of estimation can become worse when the distribution of state parameters and process/measurement noise are non-Gaussian. In nonlinear/non-Gaussian problems the particle filter which is based on sequential Monte Carlo methods can guarantee more exact estimation results. This study develops and tests the particle filter for satellite orbit determination. The particle filter can be more effective methods for satellite orbit determination in nonlinear/non-Gaussian environment.

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Fingerprint Image Enhancement using a Modified Anisotropic Gaussian Filter (개선된 Anisotropic Gaussian 필터를 이용한 지문 영상 향상)

  • 조희덕;김상희;박원우
    • Proceedings of the IEEK Conference
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    • 2003.11a
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    • pp.293-296
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    • 2003
  • The enhancement of fingerprint image is necessary to improve the performance of fingerprint recognition. The enhancement of fingerprint image with Gabor Filter(GF) is widely used. However GF has the weakness such as long processing time and the sensitivity to ridge frequency. To overcome these weaknesses, we propose a Modified Anisotropic Gaussian Filter(MAGF) which is modified from Anisotropic Filter proposed by S. Greenburg's(SAF). This proposed MAGF can reduce the calculation time of ridge frequency and improve the weakness of sensitivity to ridge frequency. We also explained that MAGF is better than others mathematically and experimentally.

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Modified Gaussian Filter based on Fuzzy Membership Function for AWGN Removal in Digital Images

  • Cheon, Bong-Won;Kim, Nam-Ho
    • 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.

Mixed Weighted Filter for Removing Gaussian and Impulse Noise

  • Yinyu, Gao;Kim, Nam-Ho
    • 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.

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Two-Dimensional Localization Problem under non-Gaussian Noise in Underwater Acoustic Sensor Networks (비가우시안 노이즈가 존재하는 수중 환경에서 2차원 위치추정)

  • Lee, DaeHee;Yang, Yeon-Mo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.23 no.5
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    • pp.418-422
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    • 2013
  • This paper has considered the location estimation problem in two dimension space by using a non-linear filter under non-Gaussian noise in underwater acoustic sensor networks(UASNs). Recently, the extended Kalman filter (EKF) is widely used in location estimation. However, the EKF has a lot of problems in the non-linear system under the non-gaussian noise environment like underwater environment. In this paper, we propose the improved Two-Dimension Particle Filter (TDPF) using the re-interpretation distribution techniques based on the maximum likelihood (ML). Through the simulation, we compared and analyzed the proposed TDPF with the EKF under the non-Gaussian underwater sensor networks. Finally, we determined that the TDPF's result shows more accurate localization than EKF's result.