Local Image Denoising Using RAISR

Theingi Zin, Shogo Seta, Yusuke Nakahara, Takuro Yamaguchi, Masaaki Ikehara

Research output: Contribution to journalArticlepeer-review

Abstract

Digital images are frequently degraded by Gaussian noise while capturing photos. This paper proposes a rapid and high accurate Gaussian noise removal method by applying the learned linear filter used in RAISR for super-resolution. The denoising methods are classified into local, nonlocal methods and deep-learning-based methods. The conventional local processing has a problem that high-frequency components of the original image are lost while reducing the noise. The nonlocal and deep-learning-based methods achieve higher denoising performance but take a long time for training and implementation. To solve these problems, we apply a super-resolution method to the local denoising method as post-processing because it can efficiently recover the high-frequency components. The super-resolution method uses a learned linear filter according to the feature of patches. The novelty of this paper is that the same processing as super-resolution is incorporated into denoising. The proposed algorithm is a rapid local denoising method and can achieve comparable performance to the high-accurate nonlocal denoising methods. Experimental results show that our proposed method provides accurate denoising performance with a low computational cost compared to nonlocal processing like BM3D.

Original languageEnglish
Pages (from-to)22420-22428
Number of pages9
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 2022

Keywords

  • Denoising
  • Gaussian noise
  • RAISR
  • joint bilateral filter
  • super-resolution

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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