Corpus ID: 228083511

TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems

  title={TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems},
  author={Kaixuan Wei and Angelica I. Avil{\'e}s-Rivero and Jingwei Liang and Ying Fu and Hua Huang and Carola-Bibiane Schonlieb},
Plug-and-Play (PnP) is a non-convex framework that combines proximal algorithms, for example alternating direction method of multipliers (ADMM), with advanced denoiser priors. Over the past few years, great empirical success has been obtained by PnP algorithms, especially for the ones integrated with deep learning-based denoisers. However, a crucial issue of PnP approaches is the need of manual parameter tweaking. As it is essential to obtain high-quality results across the high discrepancy in… Expand
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Plug-and-play (PnP) is a non-convex framework that integrates modern denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal algorithms. An advantage of PnP isExpand
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  • Computer Science
  • IEEE Transactions on Computational Imaging
  • 2019
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  • Xiran Wang, S. Chan
  • Mathematics, Computer Science
  • 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2017
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  • S. Ono
  • Mathematics, Computer Science
  • IEEE Signal Processing Letters
  • 2017
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