Wang Xin-Yu, Huang Ting-Zhu, Deng Liang-Jian
School of Mathematical Sciences/Research Center for Image and Vision Computing, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China.
PLoS One. 2018 Jan 12;13(1):e0182240. doi: 10.1371/journal.pone.0182240. eCollection 2018.
One method of solving the single-image super-resolution problem is to use Heaviside functions. This has been done previously by making a binary classification of image components as "smooth" and "non-smooth", describing these with approximated Heaviside functions (AHFs), and iteration including l1 regularization. We now introduce a new method in which the binary classification of image components is extended to different degrees of smoothness and non-smoothness, these components being represented by various classes of AHFs. Taking into account the sparsity of the non-smooth components, their coefficients are l1 regularized. In addition, to pick up more image details, the new method uses an iterative refinement for the residuals between the original low-resolution input and the downsampled resulting image. Experimental results showed that the new method is superior to the original AHF method and to four other published methods.
解决单图像超分辨率问题的一种方法是使用海维赛德函数。此前已有研究通过将图像成分进行“平滑”和“非平滑”的二元分类，用近似海维赛德函数（AHF）来描述这些成分，并进行包括l1正则化的迭代来实现。我们现在引入一种新方法，其中图像成分的二元分类扩展到不同程度的平滑和非平滑，这些成分由各类AHF表示。考虑到非平滑成分的稀疏性，对其系数进行l1正则化。此外，为了提取更多图像细节，新方法对原始低分辨率输入与下采样后的结果图像之间的残差进行迭代细化。实验结果表明，新方法优于原始AHF方法和其他四种已发表的方法。
