Modified Dual Path Network with Transform Domain Data for Image Super-Resolution

De Wei Chen, Chih Hung Kuo

Research output: Contribution to journalArticlepeer-review

Abstract

Recently, studies on single image super-resolution using Deep Convolutional Neural Networks (DCNN) have been demonstrated to have made outstanding progress over conventional signal-processing based methods. However, existing architectures have grown wider and deeper, resulting in a large amount of computation and memory cost, but only a small improvement in performance. To address this issue, in this paper, we present a Wavelet- and Saak-transform Dual Path Network (WSDPN), which considers not only low-resolution images but also transform-domain information. The proposed network exploits the rich information extracted from the transform domain to reconstruct more accurate high-resolution images. In addition, to reap the benefits from both residual network (ResNet) and densely convolutional network (DenseNet) topologies, we use dual-path blocks as the basic building blocks which allow feature re-use while ensuring the ability to continue extracting new features. Thanks to extensive research on the attention mechanism, we further introduce spatial and self-attention blocks to refine features based on feature correlations at different layers. The experimental results show that our proposed approach achieves better performance on extensive benchmark evaluation than other state-of-the-art methods.

Original languageEnglish
Article number9099286
Pages (from-to)97975-97985
Number of pages11
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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