Image Super Resolution Based on Fusing Multiple Convolution Neural Networks

Haoyu Ren, Mostafa El-Khamy, Jungwon Lee; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 54-61

Abstract


In this paper, we focus on constructing an accurate super resolution system based on multiple Convolution Neural Networks (CNNs). Each individual CNN is trained separately with different network structure. A Context-wise Network Fusion (CNF) approach is proposed to integrate the outputs of individual networks by additional convolution layers. With fine-tuning the whole fused network, the accuracy is significantly improved compared to the individual networks. We also discuss other network fusion schemes, including Pixel-Wise network Fusion (PWF) and Progressive Network Fusion (PNF). The experimental results show that the CNF outperforms PWF and PNF. Using SRCNN as individual network, the CNF network achieves the state-of-the-art accuracy on benchmark image datasets.

Related Material


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[bibtex]
@InProceedings{Ren_2017_CVPR_Workshops,
author = {Ren, Haoyu and El-Khamy, Mostafa and Lee, Jungwon},
title = {Image Super Resolution Based on Fusing Multiple Convolution Neural Networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}