Enhanced Non-Local Total Variation Model and Multi-Directional Feature Prediction Prior for Single Image Super Resolution

It is widely acknowledged that single image super-resolution (SISR) methods play a critical role in recovering the missing high-frequencies in an input low-resolution image. As SISR is severely ill-conditioned, image priors are necessary to regularize the solution spaces and generate the correspondi...

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Bibliographic Details
Published in:IEEE transactions on image processing 2019-08, Vol.28 (8), p.3778-3793
Main Authors: Chao Ren, Xiaohai He, Yifei Pu, Nguyen, Truong Q.
Format: Article
Language:English
Subjects:
Artificial neural networks
Conditioning
decaying kernel
enhanced non-local total variation
Image edge detection
Image reconstruction
Image resolution
Iterative algorithms
Iterative methods
Kernel
Learning
multi-directional feature prediction
Prediction algorithms
Reliability
Similarity
stable group similarity reliability
Super resolution
Training
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Summary:It is widely acknowledged that single image super-resolution (SISR) methods play a critical role in recovering the missing high-frequencies in an input low-resolution image. As SISR is severely ill-conditioned, image priors are necessary to regularize the solution spaces and generate the corresponding high-resolution image. In this paper, we propose an effective SISR framework based on the enhanced non-local similarity modeling and learning-based multi-directional feature prediction (ENLTV-MDFP). Since both the modeled and learned priors are exploited, the proposed ENLTV-MDFP method benefits from the complementary properties of the reconstruction-based and learning-based SISR approaches. Specifically, for the non-local similarity-based modeled prior [enhanced non-local total variation, (ENLTV)], it is characterized via the decaying kernel and stable group similarity reliability schemes. For the learned prior [multi-directional feature prediction prior, (MDFP)], it is learned via the deep convolutional neural network. The modeled prior performs well in enhancing edges and suppressing visual artifacts, while the learned prior is effective in hallucinating details from external images. Combining these two complementary priors in the MAP framework, a combined SR cost function is proposed. Finally, the combined SR problem is solved via the split Bregman iteration algorithm. Based on the extensive experiments, the proposed ENLTV-MDFP method outperforms many state-of-the-art algorithms visually and quantitatively.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2019.2902794