HNSR: Highway Networks Based Deep Convolutional Neural Networks Model for Single Image Super-Resolution

Ke Li; Bahetiyaer Bare; Bo Yan; Bailan Feng; Chunfeng Yao

ICASSP 2018accepted0 citations

HNSR: Highway Networks Based Deep Convolutional Neural Networks Model for Single Image Super-Resolution

Ke Li, Bahetiyaer Bare, Bo Yan, Bailan Feng, Chunfeng Yao

Abstract

Convolutional neural networks (CNNs) have been widely used in computer vision community. Single image super-resolution (SISR) is a classic computer vision problem, which aims to output a high-resolution image from a low-resolution one. In recent years, CNNs-based SISR methods emerged and achieved a performance leap. In this paper, we present a highly accurate deep CNNs model for SISR. Inspired by the ideas in highway networks, we propose a highway unit and cascade highway units to ensemble our model. Furthermore, we employ structural similarity index (SSIM) as a part of loss function to enhance the accuracy of trained deep CNNs model. Experimental results show that our proposed model outperforms other state-of-the-art methods.

BibTeX

@inproceedings{icassp2018_hnsrhighwaynetwo,
  title = {HNSR: Highway Networks Based Deep Convolutional Neural Networks Model for Single Image Super-Resolution},
  author = {Ke Li and Bahetiyaer Bare and Bo Yan and Bailan Feng and Chunfeng Yao},
  booktitle = {ICASSP 2018},
  year = {2018}
}