SuperResolutionCNN

Super resolution with convolutional neural network. The implementation is based on MXNet. 

Reference

[1] Dong C, Loy C C, He K, et al. Image super-resolution using deep convolutional networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 38(2): 295-307.

[2] Kim J, Kwon Lee J, Mu Lee K. Accurate image super-resolution using very deep convolutional networks[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 1646-1654.

[3] Kim J, Kwon Lee J, Mu Lee K. Deeply-recursive convolutional network for image super-resolution[C]. /Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 1637-1645.

[4] Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang. Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution[C].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017

[5] Tai Y, Yang J, Liu X. Image Super-Resolution via Deep Recursive Residual Network[C].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017

[6] Tong T, Li G, Liu X, et al. Image Super-Resolution Using Dense Skip Connections[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 4799-4807.

[7] Yang W, Feng J, Yang J, et al. Deep edge guided recurrent residual learning for image super-resolution[J]. IEEE Transactions on Image Processing, 2017, 26(12): 5895-5907.