Super-resolution methods for facial image via representation learning scheme have become very effective methods due to their efficiency. The key problem for the super-resolution of facial image is to reveal the latent relationship between the low-resolution ( LR ) and the corresponding high-resolution ( HR ) training patch pairs. To simultaneously utilize the contextual information of the target position and the manifold structure of the primitive HR space, in this work, we design a robust context-patch facial image super-resolution scheme via a kernel locality-constrained coupled-layer regression (KLC2LR) scheme to obtain the desired HR version from the acquired LR image. Here, KLC2LR proposes to acquire contextual surrounding patches to represent the target patch and adds an HR layer constraint to compensate the detail information. Additionally, KLC2LR desires to acquire more high-frequency information by searching for nearest neighbors in the HR sample space. We also utilize kernel function to map features in original low-dimensional space into a high-dimensional one to obtain potential nonlinear characteristics. Our compared experiments in the noisy and noiseless cases have verified that our suggested methodology performs better than many existing predominant facial image super-resolution methods.

中文翻译：

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核局部约束耦合层回归的鲁棒面部图像超分辨率

通过表示学习方案的人脸图像超分辨率方法由于其效率已成为非常有效的方法。人脸图像超分辨率的关键问题是揭示人脸图像之间的潜在关系低解析度(LR) 和相应的高分辨率(人力资源) 训练补丁对。为了同时利用目标位置的上下文信息和原始 HR 空间的流形结构，在这项工作中，我们通过核局部约束耦合层回归(KLC2LR) 方案从获取的 LR 图像中获得所需的 HR 版本。在这里，KLC2LR 建议获取上下文周围的补丁来表示目标补丁，并添加 HR 层约束来补偿细节信息。此外，KLC2LR 希望通过在 HR 样本空间中搜索最近的邻居来获取更多的高频信息。我们还利用核函数将原始低维空间中的特征映射到高维空间中，以获得潜在的非线性特征。我们在有噪声和无噪声情况下的比较实验验证了我们建议的方法比许多现有的主要面部图像超分辨率方法表现更好。