Inverted Residual Fourier Transformation for Lightweight Single Image Deblurring

Shunsuke Yae, Masaaki Ikehara

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

Abstract

In recent years, encoder-decoder structures are widely used for single image deblurring and successfully restore high quality images. How-ever, FLOPs and the number of parameters tend to increase to restore a high-quality image. Thus, we propose a new lightweight network (IRFTNet) based on DeepRFT. This network has two features to improve performance and lightweight. First, a new backbone called Inverted Residual Fourier Transformation block (IRFTblock) based on inverted residual block is introduced to decrease computational complexity. Second, a new module called Lower Feature Synthesis (LFS) was introduced to efficiently transfer encoder information from lower layers to upper layers. These improvements resulted in a 32.98dB in PSNR on the GoPro dataset, despite approximately half FLOPs and the number of parameters of DeepRFT.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Consumer Electronics, ICCE 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665491303
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Conference on Consumer Electronics, ICCE 2023 - Las Vegas, United States
Duration: 2023 Jan 62023 Jan 8

Publication series

NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume2023-January
ISSN (Print)0747-668X

Conference

Conference2023 IEEE International Conference on Consumer Electronics, ICCE 2023
Country/TerritoryUnited States
CityLas Vegas
Period23/1/623/1/8

Keywords

  • Fast fourier transform
  • Lightweight
  • Single image deblurring

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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