@inproceedings{659085ed34114539b51ef0e1bfdd71a0,
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.",
author = "Lu, {Wei Ting} and Lin, {Chien Wei} and Kuo, {Chih Hung} and Tung, {Ying Chan}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 ; Conference date: 12-12-2017 Through 15-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/APSIPA.2017.8282203",
language = "English",
series = "Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1160--1163",
booktitle = "Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017",
address = "United States",
}