Article ID Journal Published Year Pages File Type
412310 Neurocomputing 2014 10 Pages PDF
Abstract

•We propose a new regularization item called steering kernel regression total variation (SKRTV).•A MAP super-resolution framework is presented to assemble the global reconstruction constraint, SKRTV and NLTV terms.•We solve our model using split Bregman iterations.

This paper addresses the problem of generating a high-resolution (HR) image from a single degraded low-resolution (LR) input image without any external training set. Due to the ill-posed nature of this problem, it is necessary to find an effective prior knowledge to make it well-posed. For this purpose, we propose a novel super-resolution (SR) method based on combined total variation regularization. In the first place, we propose a new regularization term called steering kernel regression total variation (SKRTV), which exploits the local structural regularity properties in natural images. In the second place, another regularization term called non-local total variation (NLTV) is employed as a complementary term in our method, which makes the most of the redundancy of similar patches in natural images. By combining the two complementary regularization terms, we propose a maximum a posteriori probability framework of SR reconstruction. Furthermore, split Bregman iteration is applied to implement the proposed model. Extensive experiments demonstrate the effectiveness of the proposed method.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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