TMM 2017
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning
Yukai Shi, Keze Wang, Chongyu Chen, Li Xu , Liang Lin
TMM 2017

 

Efficiency


runningTime

The efficiency analysis for the scaling factor of 3 on the Set5 dataset. The evaluation platform is a high performance desktop (CPU 4.0GHz, 32GB, GTX 1080). Our proposed SPSR is written in TensorFlow and fully optimized by the Factorized CNN

 

 

Rerformance


1Quantitative comparisons among different methods in terms of PSNR (dB), in which the underline indicates the second place and bold face represents the first place.

2Quantitative comparisons among different methods in terms of SSIM, in which the underline indicates the second place and bold face represents the first place.

3

Visual comparison on the “Zebra” image from Set14 (factor 3), where the PSNR and SSIM are separated by “/”.

4

Visual comparisons on the “Butterfly” image from Set5 (factor 4), where the PSNR and SSIM are separated by “/”

 

 

References


A+ Radu Timofte, Vincent De Smet, and Luc Van Gool, “A+: Adjusted anchored neighborhood regression for fast super-resolution,” in Asian Conference on Computer Vision. Springer, 2014, pp. 111–126.
SRCNN Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang, “Learning a deep convolutional network for image super-resolution,” in Computer Vision–ECCV 2014, pp. 184–199. Springer, 2014.
SRF Samuel Schulter, Christian Leistner, and Horst Bischof, “Fast and accurate image upscaling with super-resolution forests,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3791–3799.
FSRCNN Chao Dong, Chen Change Loy, and Xiaoou Tang, “Accelerating the super-resolution convolutional neural network,” in European Conference on Computer Vision. Springer, 2016, pp. 391–407.
SCN Zhaowen Wang, Ding Liu, Jianchao Yang, Wei Han, and Thomas Huang, “Deep networks for image super-resolution with sparse prior,” arXiv preprint arXiv:1507.08905, 2015.
ShCNN Jimmy SJ. Ren, Li Xu, Qiong Yan, and Wenxiu Sun, “Shepard convolutional neural networks,” in Advances in Neural Information Processing Systems, 2015.
Factorized CNN Min Wang, Baoyuan Liu and Hassan Foroosh, “Factorized Convolutional Neural Networks,” in ICML, 2016.