Patch-Based Nonlocal Functional for Denoising Fluorescence Microscopy Image Sequences
Patch-Based Nonlocal Functional for Denoising Fluorescence Microscopy Image Sequences
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DOI：
10.1109/tmi.2009.2033991
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发表时间：
2010-02-01
影响因子：
10.6
通讯作者：
Salamero, Jean
中科院分区：
文献类型：
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作者：
Boulanger, Jerome;Kervrann, Charles;Salamero, Jean
我们提出了一种非参数回归方法去噪的3-D图像序列通过荧光显微镜。该方法利用三维+时间信息的冗余性来提高图像的信噪比。方差稳定变换首先应用于图像数据，以去除强度值的均值和方差之间的依赖性。这种预处理需要与采集系统相关的参数的知识，也估计在我们的方法。在第二步中，我们提出了一个原始的统计补丁为基础的框架降噪和保存的空间-时间的不连续性。在我们的研究中，不连续性与荧光视频显微镜中观察到的高速移动的小斑点有关。我们的想法是尽量减少一个客观的非局部能量泛函涉及时空图像补丁。最小化有一个简单的形式，并被定义为在空间变化的邻域输入数据的加权平均值。每个邻域的大小进行了优化，以提高逐点估计的性能。该算法（不需要运动估计）的性能，然后使用定性和定量的标准，在合成和真实的图像序列进行评估。
We present a nonparametric regression method for denoising 3-D image sequences acquired via fluorescence microscopy. The proposed method exploits the redundancy of the 3-D+time information to improve the signal-to-noise ratio of images corrupted by Poisson-Gaussian noise. A variance stabilization transform is first applied to the image-data to remove the dependence between the mean and variance of intensity values. This preprocessing requires the knowledge of parameters related to the acquisition system, also estimated in our approach. In a second step, we propose an original statistical patch-based framework for noise reduction and preservation of space-time discontinuities. In our study, discontinuities are related to small moving spots with high velocity observed in fluorescence video-microscopy. The idea is to minimize an objective nonlocal energy functional involving spatio-temporal image patches. The minimizer has a simple form and is defined as the weighted average of input data taken in spatially-varying neighborhoods. The size of each neighborhood is optimized to improve the performance of the pointwise estimator. The performance of the algorithm (which requires no motion estimation) is then evaluated on both synthetic and real image sequences using qualitative and quantitative criteria.