  Convolutional neural networks can automatically learn features via deep
network architectures and given input samples. However, robustness of obtained
models may have challenges in varying scenes. Bigger differences of a network
architecture are beneficial to extract more complementary structural
information to enhance robustness of an obtained super-resolution model. In
this paper, we present a heterogeneous dynamic convolutional network in image
super-resolution (HDSRNet). To capture more information, HDSRNet is implemented
by a heterogeneous parallel network. The upper network can facilitate more
contexture information via stacked heterogeneous blocks to improve effects of
image super-resolution. Each heterogeneous block is composed of a combination
of a dilated, dynamic, common convolutional layers, ReLU and residual learning
operation. It can not only adaptively adjust parameters, according to different
inputs, but also prevent long-term dependency problem. The lower network
utilizes a symmetric architecture to enhance relations of different layers to
mine more structural information, which is complementary with a upper network
for image super-resolution. The relevant experimental results show that the
proposed HDSRNet is effective to deal with image resolving. The code of HDSRNet
can be obtained at https://github.com/hellloxiaotian/HDSRNet.
