Joint bayesian convolutional sparse coding for image super-resolution

• Published in: PloS one Vol. 13; no. 9; p. e0201463
• Main Authors: Ge, Qi, Shao, Wenze, Wang, Liqian
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.09.2018; Public Library of Science (PLoS)
• Subjects: Algorithms; Animals; Bayes Theorem; Bayesian analysis; Biology and Life Sciences; Communications networks; Decomposition; Dictionaries; Engineering; Engineering and Technology; Filters; Humans; Image coding; Image Enhancement - methods; Image filters; Image Interpretation, Computer-Assisted - methods; Image processing; Image Processing, Computer-Assisted - methods; Image resolution; International conferences; Learning; Machine learning; Methods; Parameters; Pattern Recognition, Automated - methods; Pattern Recognition, Visual - physiology; Physical Sciences; Reproducibility of Results; Research and Analysis Methods; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0201463

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.