scholarly journals Quantification and reduction of Poisson-Gaussian mixed noise induced errors in ellipsometry

2021 ◽  
Author(s):  
Bo Jiang ◽  
Kai Meng ◽  
Kamal Youcef-Toumi
Keyword(s):  
Mixed Noise

Denoising of a Mixed Noise Color Image through Special Filter

2015 ◽  
Vol 2 (2) ◽  
pp. 27-42
Author(s):  
Sandeep Kumar Agarwal ◽  
◽  
Prateek Kumar ◽  
Keyword(s):  
Color Image ◽  
Mixed Noise

scholarly journals Image mixed gaussian and impulse noise elimination based on sparse representation model

2021 ◽  
Vol 23 (3) ◽  
pp. 1440
Author(s):  
Ahmed Abdulqader Hussein ◽  
Sabahaldin A. Hussain ◽  
Ahmed Hameed Reja
Keyword(s):  
Sparse Representation ◽  
Image Denoising ◽  
Impulse Noise ◽  
Heavy Tails ◽  
Noise Distribution ◽  
Self Similarity ◽  
Noise Elimination ◽  
Variational Framework ◽  
Mixed Noise ◽  
State Of Art

<p>A modified mixed Gaussian plus impulse image denoising algorithm based on weighted encoding with image sparsity and nonlocal self-similarity priors regularization is proposed in this paper. The encoding weights and the priors imposed on the images are incorporated into a variational framework to treat more complex mixed noise distribution. Such noise is characterized by heavy tails caused by impulse noise which needs to be eliminated through proper weighting of encoding residual. The outliers caused by the impulse noise has a significant effect on the encoding weights. Hence a more accurate residual encoding error initialization plays the important role in overall denoising performance, especially at high impulse noise rates. In this paper, outliers free initialization image, and an easier to implement a parameter-free procedure for updating encoding weights have been proposed. Experimental results demonstrate the capability of the proposed strategy to recover images highly corrupted by mixed Gaussian plus impulse noise as compared with the state of art denoising algorithm. The achieved results motivate us to implement the proposed algorithm in practice.</p>

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