Name
Title | ||
|---|---|---|
Image super-resolution based on error compensation with convolutional neural network. |
Abstract | ||
|---|---|---|
Convolutional Neural Networks have been widely studied for the super-resolution (SR) and other image restoration tasks. In this paper, we propose an additional error-compensational convolutional neural network (EC-CNN) that is trained based on the concept of iterative back projection (IBP). The residuals between interpolation images and ground truth images are used to train the network. This CNN model can compensate the residual projection in the IBP more accurately. This CNN-based IBP can be further combined with the super-resolution CNN(SRCNN). Experimental results show that our method can significantly enhance the quality of scale images as a post-processing method. The approach can averagely outperform SRCNN by 0.14 dB and SRCNN-EX by 0.08 dB in PSNR with scaling factor 3. |
Year | Venue | Keywords |
|---|---|---|
2017 | Asia-Pacific Signal and Information Processing Association Annual Summit and Conference | Super-resolution,Convolutional Neural Network,Iterative Back Projection |
Field | DocType | ISSN |
Iterative reconstruction,Scale factor,Residual,Pattern recognition,Convolutional neural network,Computer science,Interpolation,Ground truth,Artificial intelligence,Image restoration,Image resolution | Conference | 2309-9402 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei-Ting Lu | 1 | 0 | 0.34 |
| Chien-Wei Lin | 2 | 0 | 0.34 |
| Chih-Hung Kuo | 3 | 86 | 14.77 |
| Ying-Chan Tung | 4 | 0 | 0.34 |