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Variational probabilistic generative framework for single image super-resolution
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DOI：10.1016/j.sigpro.2018.10.004.png)
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Abstract
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In this paper, a general variational probabilistic generative framework parameterized by deep networks is proposed for single image super-resolution, which assembles the advantages of coding-based methods and regression-based methods. We use probabilistic generative networks to model the joint full likelihood of a pair of low-resolution (LR) and high-resolution (HR) patches which are generated from a shared latent representation. An inference model is applied to infer the stochastic distribution of the latent representation. By jointly optimizing the generative and inference models, a regression process to the distribution of the HR patch is implied during the learning phase, which provides an efficient forward mapping to accomplish the super-resolution task. We use our framework as a guidance and develop a new model called PGM-CP, with the help of an informative conditional prior and a consistent recognition model. We likewise show how three existing popular example-based SR methods can be reinvented under our framework. The effectiveness and efficiency of the proposed method is examined based on three public datasets. Experimental results demonstrate that our model is competitive with state-of-the-art approaches, especially when the image is corrupted by noise. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Probabilistic generative model
Image super-resolution
Conditional prior
Recognition model
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