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.

中文翻译：

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基于空间一般自回归模型的图像插值可容纳任意比例因子

提出了一种基于空间一般自回归模型的具有任意放大因子的图像插值算法。首先，为了适应任意比例因子，将非整数映射方法调制为空间通用自回归模型，该模型用于以比自回归模型更高的描述能力对分段固定模式进行建模。利用梯度角引导扩展方法扩展了空间一般自回归模型，并在邻域中包含更多像素以估计空间一般自回归模型的参数。为了实现模型参数的高精度估计，采用弹性网络的正则化方法，使目标函数的复杂度保持在合理的状态，解决了过拟合的问题。我们还引入了迭代曲率方法，以改善那些灰度强度变化较大的图像块的插值结果。用整数和非整数放大倍数对25张图像进行实验，以系统地验证该方法的客观和主观测量。该方法有效地抑制了视觉伪影，实现了任意比例因子的灵活插值方法。用整数和非整数放大倍数对25张图像进行实验，以系统地验证该方法的客观和主观测量。该方法有效地抑制了视觉伪影，实现了任意比例因子的灵活插值方法。用整数和非整数放大倍数对25张图像进行实验，以系统地验证该方法的客观和主观测量。该方法有效地抑制了视觉伪影，实现了任意比例因子的灵活插值方法。