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

作者：Yang, Bo; Xu, Huaping^*; Xie, Xiaozhen; Amjad, Kinnan

来源：Journal of Electronic Imaging, 2018, 27(5): 053030.

DOI：10.1117/1.JEI.27.5.053030

• super-resolution
• adaptive prior
• Gaussian mixture models
• whole image model

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.

• 出版日期2018-9
• 单位西北农林科技大学; 北京航空航天大学