A Heterogeneous Dynamic Convolutional Neural Network for Image
  Super-resolution
Chunwei Tian Xuanyu Zhang Jia Ren Wangmeng Zuo Yanning Zhang Chia-Wen Lin
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.