Application of Artificial Neural Network for Image Noise Level Estimation in the SVD domain
| Autor: | Emir Turajlic, Alen Begovic, Namir Skaljo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Mean squared error
Computer Networks and Communications Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION lcsh:TK7800-8360 02 engineering and technology symbols.namesake Digital image processing Singular value decomposition 0202 electrical engineering electronic engineering information engineering Image noise multilayer perceptron Electrical and Electronic Engineering block-based noise estimation lcsh:Electronics singular value decomposition noise level estimation 020206 networking & telecommunications Image segmentation Noise Additive white Gaussian noise Hardware and Architecture Control and Systems Engineering Signal Processing symbols 020201 artificial intelligence & image processing Algorithm artificial neural networks |
| Zdroj: | Electronics, Vol 8, Iss 2, p 163 (2019) Electronics Volume 8 Issue 2 |
| ISSN: | 2079-9292 |
| Popis: | The blind additive white Gaussian noise level estimation is an important and a challenging area of digital image processing with numerous applications including image denoising and image segmentation. In this paper, a novel block-based noise level estimation algorithm is proposed. The algorithm relies on the artificial neural network to perform a complex image patch analysis in the singular value decomposition (SVD) domain and to evaluate noise level estimates. The algorithm exhibits the capacity to adjust the effective singular value tail length with respect to the observed noise levels. The results of comparative analysis show that the proposed ANN-based algorithm outperforms the alternative single stage block-based noise level estimating algorithm in the SVD domain in terms of mean square error (MSE) and average error for all considered choices of block size. The most significant improvements in MSE levels are obtained at low noise levels. For some test images, such as &ldquo Car&rdquo and &ldquo Girlface&rdquo at &sigma = 1 , these improvements can be as high as 99% and 98.5%, respectively. In addition, the proposed algorithm eliminates the error-prone manual parameter fine-tuning and automates the entire noise level estimation process. |
| Databáze: | OpenAIRE |
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