Superresolution from Occluded Scenes
Superresolution from Occluded Scenes
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DOI：
10.1007/978-3-642-10684-2_3
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发表时间：
2009-12
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影响因子：
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通讯作者：
Wataru Fukuda;Atsunori Kanemura;S. Maeda;S. Ishii
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作者：
Wataru Fukuda;Atsunori Kanemura;S. Maeda;S. Ishii
我们提出了一种贝叶斯图像超分辨率方法，估计一个高分辨率的背景图像从一系列的闭塞观察。我们假设遮挡具有空间和时间连续性。例如，当卫星图像被云层遮挡或当旅游景点被人阻挡时，这种假设是合理的。虽然我们的模型的精确推理是困难的，一个有效的超分辨率算法是通过使用变分贝叶斯技术。实验表明，我们的超分辨率方法的性能优于现有的方法，不假设的闭塞或假设的闭塞，但不假设的时间连续性的闭塞。
We propose a Bayesian image superresolution method that estimates a high-resolution background image from a sequence of occluded observations. We assume that the occlusions have spatial and temporal continuities. Such assumptions would be plausible, for example, when satellite images are occluded by clouds or when a tourist site is obstructed by people. Although the exact inference of our model is difficult, an efficient superresolution algorithm is derived by using a variational Bayes technique. Experiments show that our superresolution method performs better than existing methods that do not assume the occlusions or that assume the occlusions but do not assume the temporal continuities of the occlusions.