Результаты поиска по 'image restoration':
Найдено статей: 2
  1. Pham C.T., Tran T.T., Dang H.P.
    Image noise removal method based on nonconvex total generalized variation and primal-dual algorithm
    Computer Research and Modeling, 2023, v. 15, no. 3, pp. 527-541

    In various applications, i. e., astronomical imaging, electron microscopy, and tomography, images are often damaged by Poisson noise. At the same time, the thermal motion leads to Gaussian noise. Therefore, in such applications, the image is usually corrupted by mixed Poisson – Gaussian noise.

    In this paper, we propose a novel method for recovering images corrupted by mixed Poisson – Gaussian noise. In the proposed method, we develop a total variation-based model connected with the nonconvex function and the total generalized variation regularization, which overcomes the staircase artifacts and maintains neat edges.

    Numerically, we employ the primal-dual method combined with the classical iteratively reweighted $l_1$ algorithm to solve our minimization problem. Experimental results are provided to demonstrate the superiority of our proposed model and algorithm for mixed Poisson – Gaussian removal to state-of-the-art numerical methods.

    Keywords: total variation, image restoration, mixed noise, minimization method.
  2. 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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