Spatial general autoregressive model-based image interpolation accommodates arbitrary scale factors

Yuntao Hu; Fei Hao; Chao Meng; Lili Sun; Dashuai Xu; Tianqi Zhang; Yuntao Hu; Fei Hao; Chao Meng; Lili Sun; Dashuai Xu; Tianqi Zhang

doi:10.3934/mbe.2020343

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 6: 6573-6600. doi: 10.3934/mbe.2020343

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Spatial general autoregressive model-based image interpolation accommodates arbitrary scale factors

1.
Nanjing Institute of Technology, Kangni Institute of Industrial Science and Technology, Nanjing 211167, China
2.
School of Mechanical Engineering, Nanjing Institute of Technology, Nanjing 211167, China
3.
School of Mechanical Engineering, Southeast University, Nanjing 211189, China

Received: 30 July 2020 Accepted: 14 September 2020 Published: 28 September 2020

This paper proposed a novel image interpolation algorithm with an arbitrary upscaling factor based on the spatial general autoregressive model. First, to accommodate arbitrary scale factors, a non-integer mapping method was modulated into the spatial general autoregressive model, which was employed to model the piecewise stationary pattern with a higher description capacity than autoregressive models. A gradient angle guided extension method was utilized to extend the spatial general autoregressive model, and more pixels in the neighborhood were included to estimate the parameters of the spatial general autoregressive model. To realize the high-accuracy estimation of the model parameters, a regularization method via an elastic network was adopted to maintain the complexity of the object function in a reasonable state and address the overfitting problem. We also introduced an iterative curvature method to refine the interpolation result of those image blocks with large variances of gray intensities. Experiments on 25 images were conducted with integer and non-integer magnification factors to systematically verify the objective and subjective measures of the proposed method. The visual artifacts were effectively suppressed by the proposed method, and a flexible interpolation method for arbitrary scale factors was implemented.
- image processing,
- arbitrary scale interpolation,
- autoregressive,
- gradient adaptive extension,
- elastic network,
- iterative curvature
Citation: Yuntao Hu, Fei Hao, Chao Meng, Lili Sun, Dashuai Xu, Tianqi Zhang. Spatial general autoregressive model-based image interpolation accommodates arbitrary scale factors[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 6573-6600. doi: 10.3934/mbe.2020343

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Abstract

This paper proposed a novel image interpolation algorithm with an arbitrary upscaling factor based on the spatial general autoregressive model. First, to accommodate arbitrary scale factors, a non-integer mapping method was modulated into the spatial general autoregressive model, which was employed to model the piecewise stationary pattern with a higher description capacity than autoregressive models. A gradient angle guided extension method was utilized to extend the spatial general autoregressive model, and more pixels in the neighborhood were included to estimate the parameters of the spatial general autoregressive model. To realize the high-accuracy estimation of the model parameters, a regularization method via an elastic network was adopted to maintain the complexity of the object function in a reasonable state and address the overfitting problem. We also introduced an iterative curvature method to refine the interpolation result of those image blocks with large variances of gray intensities. Experiments on 25 images were conducted with integer and non-integer magnification factors to systematically verify the objective and subjective measures of the proposed method. The visual artifacts were effectively suppressed by the proposed method, and a flexible interpolation method for arbitrary scale factors was implemented.

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