A Bayesian Nonparametric Approach to Image Super-resolution
This work addresses the problem of efficient and scalable super-resolution for image processing applications, though it appears incremental as it builds on existing Bayesian nonparametric methods.
The paper tackles image super-resolution by developing a Bayesian nonparametric model that learns dictionary elements from data, and it introduces an online variational Bayes algorithm that achieves high-quality results much faster than the Gibbs sampler.
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.