  Recent learning-based super-resolution (SR) methods often focus on dictionary
learning or network training. In this paper, we discuss in detail a new SR
method based on local patch encoding (LPE) instead of traditional dictionary
learning. The proposed method consists of a learning stage and a reconstructing
stage. In the learning stage, image patches are classified into different
classes by means of the proposed LPE, and then a projection matrix is computed
for each class by utilizing a simple constraint. In the reconstructing stage,
an input LR patch can be simply reconstructed by computing its LPE code and
then multiplying the corresponding projection matrix. Furthermore, we discuss
the relationship between the proposed method and the anchored neighborhood
regression methods; we also analyze the extendibility of the proposed method.
The experimental results on several image sets demonstrate the effectiveness of
the LPE-based methods.
