Single Image Deblurring Based on Auxiliary Sobel Loss Function

Jun Pyo Hong, Sung Jin Cho, Jeongmin Lee, Seo Won Ji, Sung Jea Ko

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

Abstract

This paper presents an effective auxiliary loss function which uses the Sobel operator to improve the performance of image deblurring methods based on deep learning. Conventional deep learning-based image deblurring methods exploit mean square error (MSE) loss function that simply measures the intensity difference in pixel-wise manner. Although recovering the lost high-frequency component is the main purpose of image deblurring, MSE loss function often fails to train the network in recovering the high-frequency components. To alleviate this issue and further improve the performance of conventional methods, we propose an auxiliary Sobel loss function which guides the network to focus on recovering the high-frequency components of resultant images. The experiment results show that the networks trained by the Sobel loss function and the conventional MSE loss function outperform the existing methods in both quantitative and qualitative evaluations.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728161648
DOIs
Publication statusPublished - 2020 Nov 1
Event2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020 - Seoul, Korea, Republic of
Duration: 2020 Nov 12020 Nov 3

Publication series

Name2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020

Conference

Conference2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020
Country/TerritoryKorea, Republic of
CitySeoul
Period20/11/120/11/3

Keywords

  • Deblur
  • Deep learning
  • Sobel operator

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation

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