【摘要】 基于稀疏表示理论,提出了一种面向单张图片超分辨率的字典学习方法。通过对训练数据进行分类,期望在每一类训练数据训练字典的过程中,增强类内的上下文信息。与之前的面向图像分类的字典学习方法所不同的是,训练数据集由高分辨率图像块和对应的低分辨率图像块共同组成,这使训练得到的字典更适用于图像重构。利用有限的训练数据集,基于上下文的字典学习方法能够提高字典表示的拓展能力,消除由多重训练数据子集带来的冗余。更多还原

【Abstract】 This paper proposed a novel dictionary learning method for single image super resolution based on sparse representation.We tried to utilize patch-level clustering to enhance the contextual information in atom learning stage.Unlike the previous dictionary learning works using the image classification,our training set is constructed from the high-resolution and low-resolution patch pairs labeled by different patch-level class,which is more appropriate for image reconstruction.This approach tried to promote the transfer ability of the dictionary which is built on a limited training set and can eliminate the atoms redundancy introduced by multiple training subsets.更多还原