Image Quality Assessment-driven Reinforcement Learning for Mixed Distorted Image Restoration

Xiaoyu ZHANG, Wei GAO*, Ge LI, Qiuping JIANG, Runmin CONG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

1 Citation (Scopus)


Due to the diversity of the degradation process that is difficult to model, the recovery of mixed distorted images is still a challenging problem. The deep learning model trained under certain degradation declines significantly in other degradation situations. In this article, we explore ways to use a combination of tools to deal with the mixed distortion. First, we illustrate the limitations of a single deep network in dealing with multiple distortion types and then introduce a hierarchical toolkit with distinguished powerful tools. Second, we investigate how an efficient representation of images combined with a reinforcement learning (RL) paradigm helps to deal with tool noise in continuous restoration. The proposed method can accurately capture the distortion preferences for selecting the optimal recovery tools by RL agent. Finally, to fully utilize random tools for unknown distortion combinations, we adopt the exploration scheme with various quality evaluation methods to achieve more quality improvements. Experimental results demonstrate that the peak signal-to-noise ratio of the proposed method is 3.30 dB higher than other state-of-the-art RL-based methods on the CSIQ single distortion dataset and 0.95 dB higher on the DIV2K mixed distortion dataset.

Original languageEnglish
Article number42
Number of pages23
JournalACM Transactions on Multimedia Computing, Communications and Applications
Issue number1
Early online date29 Apr 2022
Publication statusPublished - Jan 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 Association for Computing Machinery.


  • deep learning
  • Image restoration
  • reinforcement learning


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