Single image super resolution by multichannel densely connected convolutional network

Kang Qiu; Benshun Yi; Mian Xiang; Zheng Xiao

doi:10.1117/1.JEI.27.5.053004

10 September 2018 Single image super resolution by multichannel densely connected convolutional network

Kang Qiu, Benshun Yi, Mian Xiang, Zheng Xiao

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 5, 053004 (September 2018). https://doi.org/10.1117/1.JEI.27.5.053004

Abstract

Deep learning-based methods have shown great advantage in image super resolution (SR) compared to conventional methods. We propose a deep learning-based method named multichannel densely connected convolutional network (MDesNet) for single image SR. The proposed MDesNet first decomposes the input image into intrinsic mode functions (IMFs) and residue based on bidimensional empirical mode decomposition. Then, some densely connected convolutional subnetworks are used to exploit deep features from IMFs and residue. Since low- and high-frequency components have been decomposed into different IMFs, we can obtain accurate features from IMFs and residue by applying relatively shallow subnetwork so as to avoid vanishing gradient problem. All the features are fused in feature fusion subnetwork and output the reconstructed high-resolution image. We have compared our method with some recently proposed learning-based methods. Experimental results show that our proposed method gains the best overall quality.

Citation Download Citation

Kang Qiu, Benshun Yi, Mian Xiang, and Zheng Xiao "Single image super resolution by multichannel densely connected convolutional network," Journal of Electronic Imaging 27(5), 053004 (10 September 2018). https://doi.org/10.1117/1.JEI.27.5.053004

Received: 31 May 2018; Accepted: 21 August 2018; Published: 10 September 2018

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KEYWORDS

Super resolution

Image fusion

Lawrencium

Image enhancement

Image restoration

Visualization

Algorithm development

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