  Convolutional neural networks have been proven to be of great benefit for
single-image super-resolution (SISR). However, previous works do not make full
use of multi-scale features and ignore the inter-scale correlation between
different upsampling factors, resulting in sub-optimal performance. Instead of
blindly increasing the depth of the network, we are committed to mining image
features and learning the inter-scale correlation between different upsampling
factors. To achieve this, we propose a Multi-scale Dense Cross Network (MDCN),
which achieves great performance with fewer parameters and less execution time.
MDCN consists of multi-scale dense cross blocks (MDCBs), hierarchical feature
distillation block (HFDB), and dynamic reconstruction block (DRB). Among them,
MDCB aims to detect multi-scale features and maximize the use of image features
flow at different scales, HFDB focuses on adaptively recalibrate channel-wise
feature responses to achieve feature distillation, and DRB attempts to
reconstruct SR images with different upsampling factors in a single model. It
is worth noting that all these modules can run independently. It means that
these modules can be selectively plugged into any CNN model to improve model
performance. Extensive experiments show that MDCN achieves competitive results
in SISR, especially in the reconstruction task with multiple upsampling
factors. The code will be provided at https://github.com/MIVRC/MDCN-PyTorch.
