IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) 2020

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

Juncheng Li1     Faming Fang1     Jiaqian Li1     Kangfu Mei2     Guixu Zhang1    

1 East China Normal University     2 The Chinese University of Hong Kong (Shenzhen)    

  Contact us: cvjunchengli@gmail.con


IConvolutional 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 interscale 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.

MDCN is an improved version of our previous work MSRN.


Visual Results



Paper : [ TCSVT_MDCN.pdf ]
Experimental results : [ ]
Pre-trained model : [ ]
Source Code. : [ Code ]


    title = {MDCN: Multi-scale Dense Cross Network for Image Super-Resolution},
    author = {Li Juncheng, Fang Faming, Li Jiaqian, Mei Kangfu, and Zhang Guixu},
    booktitle = {IEEE Transactions on Circuits and Systems for Video Technology},
    year = {2020},
    publisher = {IEEE}