Dynamically unfolding recurrent restorer: A moving endpoint control method for image restoration

Xiaoshuai Zhang, Jiaying Liu, Yiping Lu, Bin Dong

Research output: Contribution to conferencePaper

4 Scopus citations

Abstract

In this paper, we propose a new control framework called the moving endpoint control to restore images corrupted by different degradation levels using a single model. The proposed control problem contains an image restoration dynamic which is modeled by a convolutional RNN. The moving endpoint, which is essentially the terminal time of the associated dynamic, is determined by a policy network. We call the proposed model the dynamically unfolding recurrent restorer (DURR). Numerical experiments show that DURR is able to achieve state-of-the-art performances on blind image denoising and JPEG image deblocking. Furthermore, DURR can well generalize to images with higher degradation levels that are not included in the training stage.

Original languageEnglish (US)
StatePublished - Jan 1 2019
Event7th International Conference on Learning Representations, ICLR 2019 - New Orleans, United States
Duration: May 6 2019May 9 2019

Conference

Conference7th International Conference on Learning Representations, ICLR 2019
CountryUnited States
CityNew Orleans
Period5/6/195/9/19

ASJC Scopus subject areas

  • Education
  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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    Zhang, X., Liu, J., Lu, Y., & Dong, B. (2019). Dynamically unfolding recurrent restorer: A moving endpoint control method for image restoration. Paper presented at 7th International Conference on Learning Representations, ICLR 2019, New Orleans, United States.