Yang Sejung, Lee Byung-Uk
Ewha Institute of Convergence Medicine, Ewha Womans University Medical Center, Seoul, Republic of Korea.
Department of Electronics Engineering, Ewha Womans University, Seoul, Republic of Korea.
PLoS One. 2015 Sep 9;10(9):e0136964. doi: 10.1371/journal.pone.0136964. eCollection 2015.
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.
在某些图像采集过程中，如荧光显微镜或天文学领域，由于各种物理限制，只能收集到有限数量的光子。所得到的图像存在与信号相关的噪声，这种噪声可建模为泊松分布，并且信噪比很低。然而，大多数关于降噪算法的研究都集中在与信号无关的高斯噪声上。在本文中，我们将噪声建模为泊松和高斯概率分布的组合，以构建更精确的模型，并采用轮廓波变换，该变换能对图像中的方向分量提供稀疏表示。我们还应用了隐马尔可夫模型，其框架能很好地描述空间和尺度间的依赖性，这些依赖性是自然图像变换系数的特性。本文提出了一种在变换域中使用轮廓波变换、隐马尔可夫模型和噪声估计的有效泊松 - 高斯噪声去噪算法。我们通过循环旋转和维纳滤波对该算法进行补充以进一步改进。我们最终展示了模拟和荧光显微镜图像的实验结果，这些结果证明了所提方法性能的提升。
