Affordable Access

deepdyve-link
Publisher Website

Single Image Super-resolution Using Gaussian Process Regression with Dictionary-based Sampling and Student-t Likelihood.

Authors
  • Wang, Haijun
  • Gao, Xinbo
  • Zhang, Kaibing
  • Li, Jie
Type
Published Article
Journal
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
May 03, 2017
Identifiers
DOI: 10.1109/TIP.2017.2700725
PMID: 28475055
Source
Medline
License
Unknown

Abstract

Gaussian process regression (GPR) is an effective statistical learning method for modeling non-linear mapping from an observed space to an expected latent space. When applying it to example learning-based super-resolution (SR), two outstanding issues remain. One is its high computational complexity restricts SR application when a large dataset is available for learning task. The other is that the commonly used Gaussian likelihood in GPR is incompatible with the true observation model for SR reconstruction. To alleviate the above two issues, we propose a GPR-based SR method by using dictionary-based sampling and student-t likelihood, called DSGPR. Considering that dictionary atoms effectively span the original training sample space, we adopt a dictionary-based sampling strategy by combining all the neighborhood samples of each atom into a compact representative training subset so as to reduce the computational complexity. Based on statistical tests, we statistically validate that Studentt likelihood is more suitable to build the observation model for SR problem. Extensive experimental results show that the proposed method outperforms other competitors and produces more pleasing details in texture regions.

Report this publication

Statistics

Seen <100 times