  Convolution neural network (CNN) has been widely used in Single Image Super
Resolution (SISR) so that SISR has been a great success recently. As the
network deepens, the learning ability of network becomes more and more
powerful. However, most SISR methods based on CNN do not make full use of
hierarchical feature and the learning ability of network. These features cannot
be extracted directly by subsequent layers, so the previous layer hierarchical
information has little impact on the output and performance of subsequent
layers relatively poor. To solve above problem, a novel Multi-Level Feature
Fusion network (MLRN) is proposed, which can take full use of global
intermediate features. We also introduce Feature Skip Fusion Block (FSFblock)
as basic module. Each block can be extracted directly to the raw multiscale
feature and fusion multi-level feature, then learn feature spatial correlation.
The correlation among the features of the holistic approach leads to a
continuous global memory of information mechanism. Extensive experiments on
public datasets show that the method proposed by MLRN can be implemented, which
is favorable performance for the most advanced methods.
