Restoring images corrupted by Poisson noise have attracted much attention in recent years due to its significant applications in image processing. There are various regularization methods of solving this problem and one of the most famous is the total variation (TV) model. In this paper, we present a new method based on accelerated alternating minimization algorithm (AAMA) which involves minimizing the sum of a Kullback–Leibler divergence term and a TV term for restoring Poisson noise degraded images. Our proposed algorithm is applied in solving the aforementioned problem and its convergence analysis is established under very weak conditions. In addition, the numerical examples reported demonstrate the efficiency and versatility of our method compared to existing methods of restoring images with Poisson noise.

中文翻译：

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泊松噪声图像恢复的加速交替最小化算法

近年来，由于泊松噪声在图像处理中的重要应用，恢复被泊松噪声破坏的图像备受关注。有多种正则化方法可以解决这个问题，其中最著名的一种是总变分 (TV) 模型。在本文中，我们提出了一种基于加速交替最小化算法 (AAMA) 的新方法，该方法涉及最小化 Kullback-Leibler 散度项和用于恢复泊松噪声退化图像的 TV 项的总和。我们提出的算法用于解决上述问题，其收敛性分析是在非常弱的条件下建立的。此外，报告的数值例子证明了我们的方法与现有的泊松噪声图像恢复方法相比的效率和多功能性。