Image super-resolution reconstruction algorithm based on Bayesian theory

The Bayesian theory provides a new solution to image super-resolution reconstruction. In view of the poor robustness to noise and motion estimation in the vast majority of superresolution reconstruction algorithms. In this paper, we propose an image super-resolution reconstruction algorithm based on...

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Bibliographic Details
Main Authors: Zheng, Wenbo, Deng, Fei, Mo, Shaocong, Jin, Xin, Qu, Yili, Zhou, Jiangwei, Zou, Rui, Shuai, Jia, Xie, Zefeng, Long, Sijie, Zheng, Chengfeng
Format: Conference Proceeding
Language:English
Subjects:
Bayes methods
Bayesian
Hyper-parameter
Image reconstruction
Mathematical model
Reconstruction algorithms
Signal to noise ratio
Similarity
Sparse image
Spatial resolution
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Summary:The Bayesian theory provides a new solution to image super-resolution reconstruction. In view of the poor robustness to noise and motion estimation in the vast majority of superresolution reconstruction algorithms. In this paper, we propose an image super-resolution reconstruction algorithm based on Bayesian representation. In the proposed algorithm, uncharted super-resolution images, motion parameters and unknown model parameters are utilized for modeling in a hierarchical Bayesian framework. We adopt degenerate distribution to derive the estimation of analytic solutions and applied the solutions to the super-resolution reconstruction which also enables the proposed algorithm robust to noises. The experimental results show that the proposed image super-resolution reconstruction algorithm based on Bayesian representation can achieve higher (or similar) performance than the state of-the-art methods.
ISSN:2158-2297
DOI:10.1109/ICIEA.2018.8398025