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Kernel Modeling Super-Resolution on Real Low-Resolution Images
Ruofan Zhou Sabine Süsstrunk
Image and Visual Representation Lab
École Polytechnique Fédérale de Lausanne
- Supplementary Material -
project page

1. X4 SR results 2. Psychovisual experiment 3. SR examples on real images 4. Experiments on zoom in SR 5. Visualization of kernels

In Section 4.3 and Section 4.4 in the paper, we report the quantitative results on X4 super-resolution, here we provide the qualitative comparsion of different SR networks. Below we show one example of X4 super-resolution results on using a Gaussian blur kernel as blur kernel:

SRCNN[3] | VDSR[4] | EDSR[1] | DBPN[2] | KMSR

Below we show one example of X4 super-resolution results on using a realistic blur kernel estimated in DPED-testing as blur kernel:

SRCNN[3] | VDSR[4] | EDSR[1] | DBPN[2] | KMSR

Reference:
[1] Lim Bee, Son Sanghyun, Kim Heewon, Nah Seungjun. Lee Kyoung Mu. "Enhanced Deep Residual Networks for Single Image Super-Resolution", in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017
[2] Haris Muhammad, Shakhnarovich Greg, Ukita Norimichi. "Deep Back-Projection Networks for Super-Resolution", in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
[3] Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. "Image Super-Resolution Using Deep Convolutional Networks", in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015
[4] Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee. "Accurate Image Super-Resolution Using Very Deep Convolutional Networks", in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016