In this paper, we propose a hybrid super-resolution method by combining global and local dictionary training in the sparse domain. In order to present and differentiate the feature mapping in different scales, a global dictionary set is trained in multiple structure scales, and a non-linear function is used to choose the appropriate dictionary to initially reconstruct the HR image. In addition, we introduce the Gaussian blur to the LR images to eliminate a widely used but inappropriate assumption that the low resolution (LR) images are generated by bicubic interpolation from high-resolution (HR) images. In order to deal with Gaussian blur, a local dictionary is generated and iteratively updated by K-means principal component analysis (K-PCA) and gradient decent (GD) to model the blur effect during the down-sampling. Compared with the state-of-the-art SR algorithms, the experimental results reveal that the proposed method can produce sharper boundaries and suppress undesired artifacts with the present of Gaussian blur. It implies that our method could be more effect in real applications and that the HR-LR mapping relation is more complicated than bicubic interpolation.

在本文中，我们提出了一种在稀疏域中结合全局和局部字典训练的混合超分辨率方法。为了呈现和区分不同比例的特征映射，在多个结构比例下训练全局字典集，并使用非线性函数选择适当的字典以初始重建HR图像。此外，我们向LR图像引入了高斯模糊，以消除广泛使用但不恰当的假设，即低分辨率（LR）图像是由高分辨率（HR）图像通过双三次插值生成的。为了处理高斯模糊，由K生成并迭代更新局部字典-表示主成分分析（K-PCA）和体面梯度（GD），以模拟下采样期间的模糊效果。与最新的SR算法相比，实验结果表明，所提出的方法在产生高斯模糊的情况下可以产生更清晰的边界并抑制不希望的伪像。这意味着我们的方法在实际应用中可能会更有效，并且HR-LR映射关系比双三次插值更为复杂。