Bayesian Methods for Image Super-Resolution
Bayesian Methods for Image Super-Resolution
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DOI：
10.1093/comjnl/bxm091
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发表时间：
2009-01-01
期刊：
影响因子：
1.4
通讯作者：
Zisserman, Andrew
中科院分区：
文献类型：
--
作者：
Pickup, Lyndsey C.;Capel, David P.;Zisserman, Andrew
我们提出了一种新的贝叶斯图像超分辨率方法，该方法对潜在参数如几何配准和光度配准以及图像的点扩散函数进行边缘处理。相关的贝叶斯超分辨率方法将高分辨率图像边缘化，需要使用不利的图像先验，而我们的方法允许更真实的图像先验分布，并显著降低了积分的维度，消除了TIPING和Bishop的贝叶斯图像超分辨率算法的主要计算瓶颈。我们在真实数据集和合成数据集上的结果说明了我们的方法的有效性。
We present a novel method of Bayesian image super-resolution in which marginalization is carried out over latent parameters such as geometric and photometric registration and the image point-spread function. Related Bayesian super-resolution approaches marginalize over the high-resolution image, necessitating the use of an unfavourable image prior, whereas our method allows for more realistic image prior distributions, and reduces the dimension of the integral considerably, removing the main computational bottleneck of algorithms such as Tipping and Bishop's Bayesian image super-resolution. We show results on real and synthetic datasets to illustrate the efficacy of our method.