A New Single Image Super-Resolution Method Based on the Infinite Mixture Model
As a powerful nonparametric Bayesian model, the infinite mixture model has been successfully used in machine learning and computer vision. The success of the infinite mixture model owes to the capability clustering and density estimation. In this paper, we propose a nonparametric Bayesian model for...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/7842566/ |
| _version_ | 1818618888156348416 |
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| author | Peitao Cheng Yuanying Qiu Xiumei Wang Ke Zhao |
| author_facet | Peitao Cheng Yuanying Qiu Xiumei Wang Ke Zhao |
| author_sort | Peitao Cheng |
| collection | DOAJ |
| description | As a powerful nonparametric Bayesian model, the infinite mixture model has been successfully used in machine learning and computer vision. The success of the infinite mixture model owes to the capability clustering and density estimation. In this paper, we propose a nonparametric Bayesian model for single-image super-resolution. Specifically, we combine the Dirichlet process and Gaussian process regression for estimating the distribution of the training patches and modeling the relationship between the low-resolution and high-resolution patches: 1) the proposed method groups the training patches by utilizing the clustering property of Dirichlet process; 2) the proposed method relates the low-resolution and high-resolution patches by predicting the property of Gaussian process; and 3) the mentioned two points are not independent but jointly learned. Hence, the proposed method can make full use of the nonparametric Bayesian model. First, the Dirichlet process mixture model is used to obtain more accurate clusters for training patches. Second, Gaussian process regression is established on each cluster, and this directly reduces the computational complexity. Finally, the two procedures are learned simultaneously to gain the suitable clusters with the ability of prediction. The parameters can be inferred simply via the Gibbs sampling technique. Thorough super-resolution experiments on various images demonstrate that the proposed method is superior to some state-of-the-art methods. |
| first_indexed | 2024-12-16T17:28:44Z |
| format | Article |
| id | doaj.art-df5ee8b5058b442fb17f5a8d076be1e9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:28:44Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-df5ee8b5058b442fb17f5a8d076be1e92022-12-21T22:22:59ZengIEEEIEEE Access2169-35362017-01-0152228224010.1109/ACCESS.2017.26641037842566A New Single Image Super-Resolution Method Based on the Infinite Mixture ModelPeitao Cheng0Yuanying Qiu1Xiumei Wang2https://orcid.org/0000-0002-2397-951XKe Zhao3School of Mechano-Electronic Engineering, Xidian University, Xi’an, ChinaSchool of Mechano-Electronic Engineering, Xidian University, Xi’an, ChinaSchool of Electronic Engineering, Xidian University, Xi’an, ChinaSchool of Mechano-Electronic Engineering, Xidian University, Xi’an, ChinaAs a powerful nonparametric Bayesian model, the infinite mixture model has been successfully used in machine learning and computer vision. The success of the infinite mixture model owes to the capability clustering and density estimation. In this paper, we propose a nonparametric Bayesian model for single-image super-resolution. Specifically, we combine the Dirichlet process and Gaussian process regression for estimating the distribution of the training patches and modeling the relationship between the low-resolution and high-resolution patches: 1) the proposed method groups the training patches by utilizing the clustering property of Dirichlet process; 2) the proposed method relates the low-resolution and high-resolution patches by predicting the property of Gaussian process; and 3) the mentioned two points are not independent but jointly learned. Hence, the proposed method can make full use of the nonparametric Bayesian model. First, the Dirichlet process mixture model is used to obtain more accurate clusters for training patches. Second, Gaussian process regression is established on each cluster, and this directly reduces the computational complexity. Finally, the two procedures are learned simultaneously to gain the suitable clusters with the ability of prediction. The parameters can be inferred simply via the Gibbs sampling technique. Thorough super-resolution experiments on various images demonstrate that the proposed method is superior to some state-of-the-art methods.https://ieeexplore.ieee.org/document/7842566/Super-resolutionDirichlet processGaussian process regressionGibbs sampling |
| spellingShingle | Peitao Cheng Yuanying Qiu Xiumei Wang Ke Zhao A New Single Image Super-Resolution Method Based on the Infinite Mixture Model IEEE Access Super-resolution Dirichlet process Gaussian process regression Gibbs sampling |
| title | A New Single Image Super-Resolution Method Based on the Infinite Mixture Model |
| title_full | A New Single Image Super-Resolution Method Based on the Infinite Mixture Model |
| title_fullStr | A New Single Image Super-Resolution Method Based on the Infinite Mixture Model |
| title_full_unstemmed | A New Single Image Super-Resolution Method Based on the Infinite Mixture Model |
| title_short | A New Single Image Super-Resolution Method Based on the Infinite Mixture Model |
| title_sort | new single image super resolution method based on the infinite mixture model |
| topic | Super-resolution Dirichlet process Gaussian process regression Gibbs sampling |
| url | https://ieeexplore.ieee.org/document/7842566/ |
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