  Image super-resolution reconstruction achieves better results than
traditional methods with the help of the powerful nonlinear representation
ability of convolution neural network. However, some existing algorithms also
have some problems, such as insufficient utilization of phased features,
ignoring the importance of early phased feature fusion to improve network
performance, and the inability of the network to pay more attention to
high-frequency information in the reconstruction process. To solve these
problems, we propose a multi-branch feature multiplexing fusion network with
mixed multi-layer attention (MBMFN), which realizes the multiple utilization of
features and the multistage fusion of different levels of features. To further
improve the networks performance, we propose a lightweight enhanced residual
channel attention (LERCA), which can not only effectively avoid the loss of
channel information but also make the network pay more attention to the key
channel information and benefit from it. Finally, the attention mechanism is
introduced into the reconstruction process to strengthen the restoration of
edge texture and other details. A large number of experiments on several
benchmark sets show that, compared with other advanced reconstruction
algorithms, our algorithm produces highly competitive objective indicators and
restores more image detail texture information.

Computer Vision and Pattern Recognition;Image and Video Processing