Weighted Anisotropic–Isotropic Total Variation for Poisson Denoising
Weighted Anisotropic–Isotropic Total Variation for Poisson Denoising
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DOI：
10.1109/icip49359.2023.10222230
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Kevin Bui;Yifei Lou;Fredrick Park;J. Xin
中科院分区：
文献类型：
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作者：
Kevin Bui;Yifei Lou;Fredrick Park;J. Xin
泊松噪声通常出现在由光子限制成像系统（诸如在天文学和医学中）捕获的图像中。由于泊松噪声的分布取决于像素强度值，因此噪声水平因像素而异。因此，对泊松破坏的图像进行去噪，同时保留重要细节可能具有挑战性。在本文中，我们提出了一个泊松去噪模型，将加权各向异性-各向同性总变差（AITV）作为正则化。然后，我们开发了一个交替方向的乘法器与一个有效的实施近端运营商的组合。最后，数值实验表明，我们的算法优于其他泊松去噪方法的图像质量和计算效率。
Poisson noise commonly occurs in images captured by photon-limited imaging systems such as in astronomy and medicine. As the distribution of Poisson noise depends on the pixel intensity value, noise levels vary from pixels to pixels. Hence, denoising a Poisson-corrupted image while preserving important details can be challenging. In this paper, we propose a Poisson denoising model by incorporating the weighted anisotropic–isotropic total variation (AITV) as a regularization. We then develop an alternating direction method of multipliers with a combination of a proximal operator for an efficient implementation. Lastly, numerical experiments demonstrate that our algorithm outperforms other Poisson denoising methods in terms of image quality and computational efficiency.