A Dictionary Learning Approach for Poisson Image Deblurring
A Dictionary Learning Approach for Poisson Image Deblurring
复制标题
泊松图像去模糊的字典学习方法
DOI：
10.1109/tmi.2013.2255883
复制
发表时间：
2013-07-01
影响因子：
10.6
通讯作者：
Zeng, Tieyong
中科院分区：
文献类型：
--
作者：
Ma, Liyan;Moisan, Lionel;Zeng, Tieyong
受模糊和泊松噪声污染的图像的恢复是医学和生物图像处理中的一个关键问题。虽然大多数现有的方法是基于变分模型，一般来自最大后验概率（MAP）制定，最近稀疏表示的图像已被证明是有效的方法，图像恢复。遵循这一思想，我们在本文中提出了一个包含三个术语的模型：基于块的稀疏表示先验学习字典，基于像素的总变差正则化术语和捕获泊松噪声统计的数据保真度术语。由此产生的优化问题可以通过交替最小化技术与变量分裂相结合来解决。大量的实验结果表明，在视觉质量，峰值信噪比值和方法噪声方面，该算法优于国家的最先进的方法。
The restoration of images corrupted by blur and Poisson noise is a key issue in medical and biological image processing. While most existing methods are based on variational models, generally derived from a maximum a posteriori (MAP) formulation, recently sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, we propose in this paper a model containing three terms: a patch-based sparse representation prior over a learned dictionary, the pixel-based total variation regularization term and a data-fidelity term capturing the statistics of Poisson noise. The resulting optimization problem can be solved by an alternating minimization technique combined with variable splitting. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio value and the method noise, the proposed algorithm outperforms state-of-the-art methods.