Feature redundancy mining: Deep light-weight image super-resolution Model

Jun Xiao, Wenqi Jia, Kin Man Lam

Research output: Journal article publicationConference articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Despite the great success achieved by deep convolutional neural network (CNN)-based models in the single image superresolution (SISR) problem, the requirement of high computational complexity, accompanied with the deep CNN models, makes it less applicable in embedded devices, e.g., mobile phones. Recently, deep light-weight models for the SISR problem have been in demand for industrial applications, and have caught the attention of many researchers. The strategies of cascading several small networks and multi-path feature extraction have shown their effectiveness in most of the existing methods. In this paper, by considering the correlation and redundancy of feature maps, we propose a feature information mining network to efficiently investigate the features, for the SISR problem. Experiment results show that our proposed model achieves the best balance between the performance and the model size, compared with other competitive deep SR models.

Original languageEnglish
Pages (from-to)1620-1624
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2021-June
DOIs
Publication statusPublished - Jun 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021

Keywords

  • Deep lightweight convolutional neural network
  • Image super-resolution

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this