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A hybrid regularizers approach based model for restoring image corrupted by Poisson noise

Tran T.T., Pham C.T.
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Image denoising is one of the fundamental problems in digital image processing. This problem usually refers to the reconstruction of an image from an observed image degraded by noise. There are many factors that cause this degradation such as transceiver equipment, or environmental influences, etc. In order to obtain higher quality images, many methods have been proposed for image denoising problem. Most image denoising method are based on total variation (TV) regularization to develop efficient algorithms for solving the related optimization problem. TV-based models have become a standard technique in image restoration with the ability to preserve image sharpness.

In this paper, we focus on Poisson noise usually appearing in photon-counting devices. We propose an effective regularization model based on combination of first-order and fractional-order total variation for image reconstruction corrupted by Poisson noise. The proposed model allows us to eliminate noise while edge preserving. An efficient alternating minimization algorithm is employed to solve the optimization problem. Finally, provided numerical results show that our proposed model can preserve more details and get higher image visual quality than recent state-of-the-art methods.

Ключевые слова: image denoising, total variation, minimization, Poisson noise
Цитата: Tran T.T., Pham C.T. A hybrid regularizers approach based model for restoring image corrupted by Poisson noise // Компьютерные исследования и моделирование, 2021, т. 13, № 5, с. 965-978
Citation in English: Tran T.T., Pham C.T. A hybrid regularizers approach based model for restoring image corrupted by Poisson noise // Computer Research and Modeling, 2021, vol. 13, no. 5, pp. 965-978
DOI: 10.20537/2076-7633-2021-13-5-965-978
URL: http://crm.ics.org.ru/journal/article/3122/
Creative Commons License Статья доступна по лицензии Creative Commons Attribution-NoDerivs 3.0 Unported License.

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