Non-blind and Blind Deconvolution Under Poisson Noise Using Fractional-Order Total Variation
Non-blind and Blind Deconvolution Under Poisson Noise Using Fractional-Order Total Variation
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DOI：
10.1007/s10851-020-00987-0
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发表时间：
2020-08-25
影响因子：
2
通讯作者：
Lou, Yifei
中科院分区：
文献类型：
--
作者：
Chowdhury, Mujibur Rahman;Qin, Jing;Lou, Yifei
在天文学、生物学和医学成像等广泛的应用中，获取的数据通常会受到泊松噪声和模糊伪影的破坏。泊松噪声经常发生时，光子计数涉及在这样的成像模式，如X射线，正电子发射断层扫描，荧光显微镜。同时，由于成像系统的物理机制，模糊也是不可避免的，其可以被建模为图像与点扩散函数的卷积。在本文中，我们考虑非盲和盲图像去模糊模型，处理泊松噪声。为了追求恢复图像的高阶光滑性，我们提出了一种分数阶全变分正则化方法，同时去除模糊和泊松噪声。我们开发了两个有效的算法的基础上交替方向法的乘子，而期望最大化算法只采用在盲情况下。各种数值实验表明，所提出的算法可以有效地重建分段光滑图像退化的泊松噪声和各种类型的模糊，包括高斯和运动模糊。特别是对于盲图像去模糊，我们获得了显着的改进，超过了现有技术。
In a wide range of applications such as astronomy, biology, and medical imaging, acquired data are usually corrupted by Poisson noise and blurring artifacts. Poisson noise often occurs when photon counting is involved in such imaging modalities as X-ray, positron emission tomography, and fluorescence microscopy. Meanwhile, blurring is also inevitable due to the physical mechanism of an imaging system, which can be modeled as a convolution of the image with a point spread function. In this paper, we consider both non-blind and blind image deblurring models that deal with Poisson noise. In the pursuit of high-order smoothness of a restored image, we propose a fractional-order total variation regularization to remove the blur and Poisson noise simultaneously. We develop two efficient algorithms based on the alternating direction method of multipliers, while an expectation-maximization algorithm is adopted only in the blind case. A variety of numerical experiments have demonstrated that the proposed algorithms can efficiently reconstruct piecewise smooth images degraded by Poisson noise and various types of blurring, including Gaussian and motion blurs. Specifically for blind image deblurring, we obtain significant improvements over the state of the art.