A MAP approach for joint motion estimation, segmentation, and super resolution
A MAP approach for joint motion estimation, segmentation, and super resolution
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DOI：
10.1109/tip.2006.888334
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发表时间：
2007-02-01
影响因子：
10.6
通讯作者：
Li, Pingxiang
中科院分区：
文献类型：
--
作者：
Shen, Huanfeng;Zhang, Liangpei;Li, Pingxiang
超分辨率图像重建允许从多个有噪声、模糊和下采样的低分辨率图像中恢复高分辨率 (HR) 图像。在本文中，我们提出了一个复杂的超分辨率问题的联合公式，其中场景包含多个独立移动的对象。该公式建立在最大后验 (MAP) 框架之上，该框架明智地将运动估计、分割和超分辨率结合在一起。循环坐标下降优化过程用于求解 MAP 公式，其中运动场、分割场和 HR 图像分别在给定其他两个的情况下以交替方式找到。具体来说，采用基于梯度的方法来求解HR图像和运动场，并采用迭代条件模式优化方法来获得分割场。所提出的算法已使用合成图像序列、“移动和日历”序列以及原始“摩托车和汽车”序列进行了测试。实验结果和误差分析验证了该算法的有效性。
Super resolution image reconstruction allows the recovery of a high-resolution (HR) image from several low-resolution images that are noisy, blurred, and down sampled. In this paper, we present a joint formulation for a complex super-resolution problem in which the scenes contain multiple independently moving objects. This formulation is built upon the maximum a posteriori (MAP) framework, which judiciously combines motion estimation, segmentation, and super resolution together. A cyclic coordinate descent optimization procedure is used to solve the MAP formulation, in which the motion fields, segmentation fields, and HR images are found in an alternate manner given the two others, respectively. Specifically, the gradient-based methods are employed to solve the HR image and motion fields, and an iterated conditional mode optimization method to obtain the segmentation fields. The proposed algorithm has been tested using a synthetic image sequence, the "Mobile and Calendar" sequence, and the original "Motorcycle and Car" sequence. The experiment results and error analyses verify the efficacy of this algorithm.