Abstract

Single-image super-resolution reconstruction aims to obtain a high-resolution image from a low-resolution image. Since the super-resolution problem is ill-posed, it is common to use a regularization technique. However, the choice of the fidelity and regularization terms is not obvious, and it plays a major role in the quality of the desired high resolution image. In this paper, a hybrid single-image super-resolution model integrated with total variation (TV) and fractional-order TV is proposed to provide an effective reconstruction of the HR image. We develop an efficient numerical scheme for this model using the scalar auxiliary variable approach with an adaptive time stepping strategy. Thorough experimental results suggest that the proposed model and numerical scheme can reconstruct high quality results both quantitatively and perceptually.

中文翻译：

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图像超分辨率的总分数阶变化模型及其SAV算法

摘要

单图像超分辨率重建旨在从低分辨率图像获得高分辨率图像。由于超分辨率问题是不适当的，因此通常使用正则化技术。但是，保真度和正则项的选择并不明显，它在所需的高分辨率图像的质量中起着重要作用。本文提出了一种将总变化量（TV）和分数阶TV相结合的混合单图像超分辨率模型，以提供HR图像的有效重建。我们使用标量辅助变量方法和自适应时间步长策略为该模型开发了一种有效的数值方案。全面的实验结果表明，所提出的模型和数值方案可以定量和感知地重建高质量的结果。