Adaptive total variation L1 regularization for salt and pepper image denoising

In this article, we propose an adaptive total variation (TV) regularization model for salt and pepper denoising in digital images. The adaptive TV denoising method is developed based on the general regularized image restoration model with L1 fidelity for handling salt and pepper noise model. An esti...

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Bibliographic Details
Published in:Optik (Stuttgart) 2020-04, Vol.208, p.163677, Article 163677
Main Authors: Thanh, Dang Ngoc Hoang, Thanh, Le Thi, Hien, Nguyen Ngoc, Prasath, Surya
Format: Article
Language:English
Subjects:
Adaptive image denoising
Image denoising
Image quality assessment
Image restoration
Primal dual gradient
Salt and pepper noise
Total variation
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Summary:In this article, we propose an adaptive total variation (TV) regularization model for salt and pepper denoising in digital images. The adaptive TV denoising method is developed based on the general regularized image restoration model with L1 fidelity for handling salt and pepper noise model. An estimation for regularization parameter is also proposed based on the characteristics of the salt and pepper noise. We implement the proposed adaptive TV-L1 regularization model efficiently for image denoising using the primal dual gradient method. In the experiments, the full-reference image quality assessment metrics are used for evaluating denoising quality across various noise levels in different synthetic and real images. The denoising results are compared to other similar salt and pepper image denoising methods and our results indicate we obtain artifact free edge preserving restorations.
ISSN:0030-4026
1618-1336
DOI:10.1016/j.ijleo.2019.163677