Embedded local adaptive Gaussian mixture models for whole image super-resolution

Bo Yang; Huaping Xu; Xiaozhen Xie; Kinnan Amjad

doi:10.1117/1.JEI.27.5.053030

8 October 2018 Embedded local adaptive Gaussian mixture models for whole image super-resolution

Bo Yang, Huaping Xu, Xiaozhen Xie, Kinnan Amjad

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 5, 053030 (October 2018). https://doi.org/10.1117/1.JEI.27.5.053030

Abstract

Conventional learning-based methods for super-resolution (SR) have proven efficient for their potential of recovering the local details on low-resolution (LR) sharp images. Adaptive Gaussian mixture models (AGMMs) specialized for SR are developed based on the assumption that the corresponding high- and low-resolution image patches can be jointly modeled by GMMs. As a regularization term, the AGMMs are then embedded into the whole image SR models. The embedded AGMMs (EAGMMs) outperform the simple whole image SR models greatly. In addition, EAGMMs can also serve as a common framework for SR on LR blurred images. We have evaluated both AGMMs and EAGMMs on a variety of test images, and obtained very promising and competitive performance. In most cases, AGMMs and EAGMMs generate better results than the state-of-the-art SR methods.

Citation Download Citation

Bo Yang, Huaping Xu, Xiaozhen Xie, and Kinnan Amjad "Embedded local adaptive Gaussian mixture models for whole image super-resolution," Journal of Electronic Imaging 27(5), 053030 (8 October 2018). https://doi.org/10.1117/1.JEI.27.5.053030

Received: 21 March 2018; Accepted: 12 September 2018; Published: 8 October 2018

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available