Poisson-Gaussian Noise Reduction Using the Hidden Markov Model in Contourlet Domain for Fluorescence Microscopy Images.
Poisson-Gaussian Noise Reduction Using the Hidden Markov Model in Contourlet Domain for Fluorescence Microscopy Images.
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DOI：
10.1371/journal.pone.0136964
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Lee BU
中科院分区：
文献类型：
--
作者：
Yang S;Lee BU
在某些图像采集过程中，如在荧光显微镜或天文学中，由于各种物理约束，只能收集有限数量的光子。所得到的图像受到信号相关噪声的影响，其可以被建模为泊松分布，以及低信噪比。然而，大多数降噪算法的研究集中在信号无关的高斯噪声。在本文中，我们模型噪声的泊松和高斯概率分布的组合，以构建一个更准确的模型，并采用轮廓波变换，它提供了一个稀疏表示的图像中的方向分量。我们还应用隐马尔可夫模型的框架，巧妙地描述了空间和尺度间的依赖关系，这是自然图像的变换系数的属性。本文利用轮廓波变换、隐马尔可夫模型和变换域噪声估计，提出了一种有效的泊松-高斯噪声去噪算法。为了进一步改进算法，我们采用循环旋转和维纳滤波对算法进行了补充。最后，我们展示了实验结果与模拟和荧光显微镜图像，证明了所提出的方法的性能提高。
In certain image acquisitions processes, like in fluorescence microscopy or astronomy, only a limited number of photons can be collected due to various physical constraints. The resulting images suffer from signal dependent noise, which can be modeled as a Poisson distribution, and a low signal-to-noise ratio. However, the majority of research on noise reduction algorithms focuses on signal independent Gaussian noise. In this paper, we model noise as a combination of Poisson and Gaussian probability distributions to construct a more accurate model and adopt the contourlet transform which provides a sparse representation of the directional components in images. We also apply hidden Markov models with a framework that neatly describes the spatial and interscale dependencies which are the properties of transformation coefficients of natural images. In this paper, an effective denoising algorithm for Poisson-Gaussian noise is proposed using the contourlet transform, hidden Markov models and noise estimation in the transform domain. We supplement the algorithm by cycle spinning and Wiener filtering for further improvements. We finally show experimental results with simulations and fluorescence microscopy images which demonstrate the improved performance of the proposed approach.