Deep Orthogonal Transform Feature for Image Denoising

Yoon Ho Shin, Min Je Park, Oh Young Lee, Jong Ok Kim

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Recently, CNN-based image denoising has been investigated and shows better performance than conventional vision based techniques. However, there are still a couple of limits that are weak partly in restoring image details like textured regions or produce other artifacts. In this paper, we introduce noise-separable orthogonal transform features into a neural denoising framework. We specifically choose wavelet and PCA as an orthogonal transform, which achieved a good denoising performance conventionally. In addition to spatial image signals, the orthogonal transform features (OTFs) are fed into a denoising network. For the guide of the denoising process, we also concatenate OTFs from the image denoised by the existing method. This can play a role of prior for learning a denoising process. It has been confirmed that our proposed multi-input network can achieve better denoising performance than other single-input networks.

Original languageEnglish
Article number9062563
Pages (from-to)66898-66909
Number of pages12
JournalIEEE Access
Publication statusPublished - 2020


  • Image denoising
  • PCA
  • deep learning for image denoising
  • multi-input network
  • orthogonal transform
  • wavelet transform

ASJC Scopus subject areas

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


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