Mixed Noise Image De-Noising Based on EM Algorithm

Obtaining high quality images is very important in many areas of applied sciences, but images are usually polluted by noise in the process of generation, transmission and acquisition. In recent years, wavelet analysis achieves significant results in the field of image de-noising. However, most of the studies of noise-induced phenomena assume that the noise source is Gaussian. The use of mixed Gaussian and impulse noise is rare, mainly because of the difficulties in handling them. In the process of image de-noising, the noise model’s parameter estimation is a key issue, because the accuracy of the noise model’s parameters could affect the de-noising quality. In the case of mixed Gaussian noises, EM algorithm is an iterative algorithm, which simplifies the maximum likelihood equation. This thesis takes wavelet analysis and statistics theory as tools, studies on mixed noise image de-noising, provides two classes of algorithms for dealing with a special type of non-Gaussian noise, mixed Gaussian and Pepper & Salt noise.

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An Variable Coefficient Images Denoising Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.832 ◽

2013 ◽

Vol 333-335 ◽

pp. 832-835

Author(s):

Da Peng Zhou

Keyword(s):

Gaussian Noise ◽

Variable Coefficient ◽

Impulse Noise ◽

Denoising Method ◽

Noise Image ◽

Black Noise ◽

Gray Values ◽

Average Filter ◽

Coefficient Method

Proposed An images denoising method by using variable coefficient in filtering. The key point is using different coefficient to minimize the influence of each gray values of the pixels as is regarded as black noise and white noise and choosing a threshold to decided which filter will be used as the most suitable one. Usually,a image is polluted by Gaussian noise and impulse noise simultaneously, It cannot be obtained if only using one of them,though both median and average filter are used inimage denoising in obtain the edge as well as detail characteristics of impulse noise image and smoothing Gaussiannoise respectively when denoising in spatial space. Experimental results show that the variable coefficient method can improve the quality of images than the classical methods.

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Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

Remote Sensing ◽

10.3390/rs10101600 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1600 ◽

Cited By ~ 5

Author(s):

Chang Li ◽

Yu Liu ◽

Juan Cheng ◽

Rencheng Song ◽

Hu Peng ◽

...

Keyword(s):

Gaussian Noise ◽

Impulse Noise ◽

Hyperspectral Image ◽

State Of The Art ◽

Additive White Gaussian Noise ◽

Bilinear Model ◽

Hyperspectral Unmixing ◽

Noise Impulse ◽

Mixed Noise ◽

Alternative Direction Method

Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.

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Signal processing in non-Gaussian noise using mixture distributions and the EM algorithm

Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136) ◽

10.1109/acssc.1997.680365 ◽

2002 ◽

Cited By ~ 10

Author(s):

R.J. Kozick ◽

R.S. Blum ◽

B.M. Sadler

Keyword(s):

Signal Processing ◽

Em Algorithm ◽

Gaussian Noise ◽

Mixture Distributions ◽

The Em Algorithm ◽

Non Gaussian

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A Mixed Noise Filtering Algorithm for High-Speed Sequence Image Processing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.415.318 ◽

2013 ◽

Vol 415 ◽

pp. 318-324

Author(s):

Yan Zhu Yang ◽

Wei Liang Liu ◽

Neng Jie Chen ◽

Wei Zhu

Keyword(s):

Image Processing ◽

Gaussian Noise ◽

High Speed ◽

Impulse Noise ◽

Effective Means ◽

Weighted Average ◽

Frame Rate ◽

Good Effect ◽

Filtering Algorithm ◽

Mixed Noise

t is a effective means by using high-speed vision to locate mobile targets. Under the circumstance of high frame rate and high sensitivity (300Hz), in addition to the Gaussian noise and impulse noise, the image quality is also influenced by atmospheric instability, and it is mainly expressed as Gaussian noise. An improved adaptive threshold weighted mean (IATWM) de-noising algorithm is proposed in this paper. According to the characteristics of impulse noise, the algorithm is able to obtain the threshold adaptively and separate the impulse noise. Then, the weighted median filtering algorithm is used to remove the impulse noise. And the improved weighted average filter algorithm is adopted to remove the Gaussian noise for graphics with Gaussian noise. The algorithm could deal with the Gaussian noise and impulse noise separately, avoiding the weaken handling for the parts not subject to pixels pollution of the impulse noise. The experimental results show that the processing result of the algorithm is able to retain the image details, superior to the traditional filtering algorithms and MTM algorithm. In addition, the algorithm provides an effective way to eliminate the mixed noise, along with a good effect on the high-speed sequence image processing.

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