Spatially Adaptive Image Deblurring Module Based on Dilated Deformable Convolutions

Hea Bin Yang, Sung Jin Cho, Sung Jea Ko

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

Abstract

In this paper, we propose an image deblurring module based on modified deformable convolutions (MDCs) to deal with spatially varying blur. The proposed method first reduces the number of channels of the input feature map. Then, we extract spatially adapted features by applying a MDC that uses two 1D convolution kernels for the deformable offsets. An additional MDC with dilation enlarges the receptive field and further strengthens features. We fully preserve previous features and propagate them using shortcut connection. Experimental results show that the model incorporating our modules outperforms the baseline model.

Original languageEnglish
Title of host publication2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350320213
DOIs
Publication statusPublished - 2023
Event2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 - Singapore, Singapore
Duration: 2023 Feb 52023 Feb 8

Publication series

Name2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

Conference

Conference2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
Country/TerritorySingapore
CitySingapore
Period23/2/523/2/8

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This research was funded and supported by LG Display CO., Ltd.

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Image deblurring
  • image processing
  • neural network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Electrical and Electronic Engineering
  • Control and Optimization

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