scholarly journals Multi‐frame blind deconvolution of atmospheric turbulence degraded images with mixed noise models

2018 ◽  
Vol 54 (4) ◽  
pp. 206-208 ◽  
Author(s):  
Afeng Yang ◽  
Xue Jiang ◽  
David Day‐Uei Li
Keyword(s):  
Atmospheric Turbulence ◽  
Blind Deconvolution ◽  
Mixed Noise ◽  
Degraded Images ◽  
Noise Models

scholarly journals Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Paul Rodríguez
Keyword(s):  
Total Variation ◽  
Blind Deconvolution ◽  
Numerical Algorithms ◽  
Total Variation Regularization ◽  
General Technique ◽  
Denoising Method ◽  
Tv Regularization ◽  
Mixed Noise ◽  
Map Estimator ◽  
Noise Models

Total Variation (TV) regularization has evolved from an image denoising method for images corrupted with Gaussian noise into a more general technique for inverse problems such as deblurring, blind deconvolution, and inpainting, which also encompasses the Impulse, Poisson, Speckle, and mixed noise models. This paper focuses on giving a summary of the most relevant TV numerical algorithms for solving the restoration problem for grayscale/color images corrupted with several noise models, that is, Gaussian, Salt & Pepper, Poisson, and Speckle (Gamma) noise models as well as for the mixed noise scenarios, such the mixed Gaussian and impulse model. We also include the description of the maximum a posteriori (MAP) estimator for each model as well as a summary of general optimization procedures that are typically used to solve the TV problem.

scholarly journals LSSVR Model of G-L Mixed Noise-Characteristic with Its Applications

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 629 ◽  
Author(s):  
Shiguang Zhang ◽  
Ting Zhou ◽  
Lin Sun ◽  
Wei Wang ◽  
Baofang Chang
Keyword(s):  
Wind Speed ◽  
Regression Models ◽  
Historical Data ◽  
Least Square ◽  
Noise Model ◽  
Noise Distribution ◽  
Short Term ◽  
Mixed Noise ◽  
Noise Models ◽  
Better Than

Due to the complexity of wind speed, it has been reported that mixed-noise models, constituted by multiple noise distributions, perform better than single-noise models. However, most existing regression models suppose that the noise distribution is single. Therefore, we study the Least square S V R of the Gaussian–Laplacian mixed homoscedastic ( G L M − L S S V R ) and heteroscedastic noise ( G L M H − L S S V R ) for complicated or unknown noise distributions. The ALM technique is used to solve model G L M − L S S V R . G L M − L S S V R is used to predict short-term wind speed with historical data. The prediction results indicate that the presented model is superior to the single-noise model, and has fine performance.

Restoring atmospheric-turbulence-degraded images

2016 ◽  
Vol 55 (19) ◽  
pp. 5082 ◽  
Author(s):  
Md. Hasan Furhad ◽  
Murat Tahtali ◽  
Andrew Lambert
Keyword(s):  
Atmospheric Turbulence ◽  
Degraded Images
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