  Deep convolutional neural networks (CNNs) with strong expressive ability have
achieved impressive performances on single image super-resolution (SISR).
However, their excessive amounts of convolutions and parameters usually consume
high computational cost and more memory storage for training a SR model, which
limits their applications to SR with resource-constrained devices in real
world. To resolve these problems, we propose a lightweight enhanced SR CNN
(LESRCNN) with three successive sub-blocks, an information extraction and
enhancement block (IEEB), a reconstruction block (RB) and an information
refinement block (IRB). Specifically, the IEEB extracts hierarchical
low-resolution (LR) features and aggregates the obtained features step-by-step
to increase the memory ability of the shallow layers on deep layers for SISR.
To remove redundant information obtained, a heterogeneous architecture is
adopted in the IEEB. After that, the RB converts low-frequency features into
high-frequency features by fusing global and local features, which is
complementary with the IEEB in tackling the long-term dependency problem.
Finally, the IRB uses coarse high-frequency features from the RB to learn more
accurate SR features and construct a SR image. The proposed LESRCNN can obtain
a high-quality image by a model for different scales. Extensive experiments
demonstrate that the proposed LESRCNN outperforms state-of-the-arts on SISR in
terms of qualitative and quantitative evaluation. The code of LESRCNN is
accessible on https://github.com/hellloxiaotian/LESRCNN.
