Open AccessSpatial general autoregressive model-based image interpolation accommodates arbitrary scale factors
Author(s) -
Yuntao Hu,
Fei Hao,
Chao Meng,
Lili Sun,
Dashuai Xu,
Tianqi Zhang
Publication year - 2020
Publication title -
mathematical biosciences and engineering
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2020343
Subject(s) - autoregressive model , overfitting , interpolation (computer graphics) , star model , mathematics , image scaling , algorithm , piecewise , integer (computer science) , nearest neighbor interpolation , pixel , scale (ratio) , computer science , multivariate interpolation , mathematical optimization , artificial intelligence , image (mathematics) , bilinear interpolation , image processing , statistics , autoregressive integrated moving average , time series , artificial neural network , mathematical analysis , physics , quantum mechanics , programming language
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.