2018 JRNL PP Yuanfei Huang - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Example learning-based single image superresolution
(SR) is a promising method for reconstructing a highresolution
(HR) image from a single-input low-resolution (LR)
image. Lots of popular SR approaches are more likely either timeor
space-intensive, which limit their practical applications. Hence,
some research has focused on a subspace view and delivered stateof-
the-art results. In this paper, we utilize an effective way with
mixture prior models to transform the large nonlinear feature
space of LR images into a group of linear subspaces in the
training phase. In particular, we first partition image patches
into several groups by a novel selective patch processing method
based on difference curvature of LR patches, and then learning
the mixture prior models in each group. Moreover, different prior
distributions have various effectiveness in SR, and in this case,
we find that student-t prior shows stronger performance than
the well-known Gaussian prior. In the testing phase, we adopt
the learned multiple mixture prior models to map the input LR
features into the appropriate subspace, and finally reconstruct
the corresponding HR image in a novel mixed matching way.
Experimental results indicate that the proposed approach is both
quantitatively and qualitatively superior to some state-of-the-art
SR methods.