Open AccessJoint bayesian convolutional sparse coding for image super-resolution
Author(s) -
Qi Ge,
Wei Shao,
Liqian Wang
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0201463
Subject(s) - neural coding , computer science , artificial intelligence , bayesian inference , pattern recognition (psychology) , convolutional code , inference , convolutional neural network , bayesian probability , bayes' theorem , coding (social sciences) , algorithm , mathematics , decoding methods , statistics
We propose a convolutional sparse coding (CSC) for super resolution (CSC-SR) algorithm with a joint Bayesian learning strategy. Due to the unknown parameters in solving CSC-SR, the performance of the algorithm depends on the choice of the parameter. To this end, a coupled Beta-Bernoulli process is employed to infer appropriate filters and sparse coding maps (SCM) for both low resolution (LR) image and high resolution (HR) image. The filters and the SCMs are learned in a joint inference. The experimental results validate the advantages of the proposed approach over the previous CSC-SR and other state-of-the-art SR methods.