Текущий выпуск Номер 5, 2024 Том 16

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Результаты поиска по 'image denoising':
Найдено статей: 1
  1. 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

    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.
    Tran T.T., Pham C.T.
    A hybrid regularizers approach based model for restoring image corrupted by Poisson noise
    Computer Research and Modeling, 2021, v. 13, no. 5, pp. 965-978

    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.

    Keywords: image denoising, total variation, minimization, Poisson noise.

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Журнал включен в базу данных Russian Science Citation Index (RSCI) на платформе Web of Science

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