Learning local Gaussian process regression for image super-resolution

Li, Jianmin; Qu, Yanyun; Li, Cuihua; Xie, Yuan; Wu, Yang; Fan, Jianping

doi:10.1016/j.neucom.2014.11.064

Cited by 16 publications

(5 citation statements)

References 24 publications

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“…Xie [51] proposed super resolution technique based on GPR and K-means clustering algorithm to group the training patch as per the local geometric structure of low resolution patches. In this method square exponential covariance function is used find the similarity between image patches.…”

Section: Spatial Domain-based Super Resolutionmentioning

confidence: 99%

Survey of Super Resolution Techniques

Deshpande¹,

Patavardhan²

2019

IJIVP

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The key objective of super-resolution imaging is the process of reconstructing a high resolution image from one or a set of low resolution images, to overcome the ill-posed conditions of the image capturing process to get better visualization. It has found practical applications in many real world problems such as surveillance, medical imaging, text image analysis, biometrics, satellite imaging, to name a few. This has encouraged many researchers to develop a new super resolution algorithm for a particular purpose. The objective of the review paper is to explore the different image super resolution algorithms used to enhance the low resolution images to high resolution images. This survey on super resolution algorithms will help the researchers to comprehend the effectiveness of the super resolution process and will make easy to develop advanced super resolution methods.

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Section: Spatial Domain-based Super Resolutionmentioning

confidence: 99%

Survey of Super Resolution Techniques

Deshpande¹,

Patavardhan²

2019

IJIVP

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“…where, P s i s j (m) is approximated by the state transition frequency [20] and m represents the number of steps of the structure element movement. In this paper, one-step transition probability is used, and thus, m = 1.…”

Section: Structural Element Transition Probabilitymentioning

confidence: 99%

Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements

Kang

Zhang

et al. 2019

Applied Sciences

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Coal seam thickness prediction is crucial in coal mine design and coal mining. In order to improve the prediction accuracy, an improved Kriging interpolation method on the basis of efficient data and Radial Basis Function (RBF-Kriging) is firstly proposed to interpolate the cutting data that is obtained in pre-mining, especially at the edge of the geological surface of coal seam by taking into account the spatial structure and the efficient spatial range, ensuring the integrity of the edge data during the movement of structural elements. Subsequently, a structural element transition probability based Gaussian process progression (STTP-GPR) method is proposed to predict the coal seam thickness from the interpolated coal seam data. The experimental results demonstrated that the proposed STTP-GPR method has superior performance in coal seam thickness prediction. The average absolute error of thickness prediction for thin coal seams is 0.025 m, which significantly improves the prediction accuracy in comparison to the existing back propagation (BP) neural networks, support vector machine, and Gaussian process regression methods.

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“…Gaussian process regression (GPR) has been gradually recognized as a useful soft sensor modeling method over the past few years due to its excellent performance [18], [19]. Compared with common machine learning algorithms including SVM, ANN and regression trees, GPR has good adaptability and strong generalization ability to deal with complex problems such as high dimensions, small samples and nonlinearity.…”

Section: Introductionmentioning

confidence: 99%