Image super-resolution based on error compensation with convolutional neural network

Wei Ting Lu, Chien Wei Lin, Chih Hung Kuo, Ying Chan Tung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Convolutional Neural Networks have been widely studied for the super-resolution (SR) and other image restoration tasks. In this paper, we propose an additional error-compensational convolutional neural network (EC-CNN) that is trained based on the concept of iterative back projection (IBP). The residuals between interpolation images and ground truth images are used to train the network. This CNN model can compensate the residual projection in the IBP more accurately. This CNN-based IBP can be further combined with the super-resolution CNN(SRCNN). Experimental results show that our method can significantly enhance the quality of scale images as a post-processing method. The approach can averagely outperform SRCNN by 0.14 dB and SRCNN-EX by 0.08 dB in PSNR with scaling factor 3.

Original languageEnglish
Title of host publicationProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1160-1163
Number of pages4
ISBN (Electronic)9781538615423
DOIs
Publication statusPublished - 2018 Feb 5
Event9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 - Kuala Lumpur, Malaysia
Duration: 2017 Dec 122017 Dec 15

Publication series

NameProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Volume2018-February

Other

Other9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
CountryMalaysia
CityKuala Lumpur
Period17-12-1217-12-15

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Information Systems
  • Signal Processing

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