标题： 一种贝叶斯非参数图像超分辨率方法 显示英文标题

标题： A Bayesian Nonparametric Approach to Image Super-resolution

摘要： 从低分辨率图像中生成高分辨率图像的超分辨率方法。 在本文中，我们开发了一种新的贝叶斯非参数模型用于超分辨率。 我们的方法使用一个beta-Bernoulli过程从数据中学习一组重复的视觉模式，称为字典元素。 由于它是非参数的，找到的元素数量也由数据确定。 我们在基准图像和自然图像上测试结果，并与其他几种研究文献中的模型进行比较。 我们进行了大规模的人类评估实验来评估结果的视觉质量。 在第一次实现中，我们使用Gibbs采样来近似后验分布。 然而，该算法对于大规模数据不可行。 为了解决这个问题，我们随后开发了一种在线变分贝叶斯（VB）算法。 该算法在比Gibbs采样所需时间少得多的时间内找到高质量的字典。 显示英文摘要

摘要： Super-resolution methods form high-resolution images from low-resolution images. In this paper, we develop a new Bayesian nonparametric model for super-resolution. Our method uses a beta-Bernoulli process to learn a set of recurring visual patterns, called dictionary elements, from the data. Because it is nonparametric, the number of elements found is also determined from the data. We test the results on both benchmark and natural images, comparing with several other models from the research literature. We perform large-scale human evaluation experiments to assess the visual quality of the results. In a first implementation, we use Gibbs sampling to approximate the posterior. However, this algorithm is not feasible for large-scale data. To circumvent this, we then develop an online variational Bayes (VB) algorithm. This algorithm finds high quality dictionaries in a fraction of the time needed by the Gibbs sampler.