MDCN: Multi-scale Dense Cross Network for Image Super-Resolution

by   Juncheng Li, et al.

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


page 1

page 3

page 4

page 8

page 9

page 10

page 11

page 12


Multi-scale deep neural networks for real image super-resolution

Single image super-resolution (SR) is extremely difficult if the upscali...

Multi-Scale Hourglass Hierarchical Fusion Network for Single Image Deraining

Rain streaks bring serious blurring and visual quality degradation, whic...

OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network

Super-resolution (SR) has achieved great success due to the development ...

Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution

Multi-scale techniques have achieved great success in a wide range of co...

Scale-Aware Dynamic Network for Continuous-Scale Super-Resolution

Single-image super-resolution (SR) with fixed and discrete scale factors...

Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning

Deep convolutional neural networks have been demonstrated to be effectiv...

Multi-grained Attention Networks for Single Image Super-Resolution

Deep Convolutional Neural Networks (CNN) have drawn great attention in i...

Please sign up or login with your details

Forgot password? Click here to reset