Deep Multi-Level Wavelet-CNN Denoiser Prior for Restoring Blurred Image With Cauchy Noise

Tingting Wu, Wei Li, Shilong Jia, Yiqiu Dong, Tieyong Zeng

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Cauchy noise, as a typical non-Gaussian noise, appears frequently in many important fields, such as radar, medical, and biomedical imaging. In this letter, we focus on image recovery under Cauchy noise. Instead of the celebrated total variation or low-rank prior, we adopt a novel deep-learning-based image denoiser prior to effectively remove Cauchy noise with blur. To preserve more detailed texture and better balance between the receptive field size and the computational cost, we apply the multi-level wavelet convolutional neural network (MWCNN) to train this denoiser. We use the forward-backward splitting (FBS) method to handle the proposed model, which can be implemented efficiently without introducing auxiliary variables. Moreover, the multi-noise-levels strategy is employed to train a series of denoisers to restore the image corrupted by Cauchy noise and blur. Numerical experiments demonstrate clearly that our method has better performance than the existing image restoration methods for removing Cauchy noise in terms of the quantitative index and visual quality.
Original languageEnglish
JournalIEEE Signal Processing Letters
Volume27
Pages (from-to)1635-1639
ISSN1070-9908
DOIs
Publication statusPublished - 2020

Keywords

  • Cauchy noise
  • Forward-backward splitting
  • Image deblurring
  • mwcnn denoiser

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