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

Published: 03 February 2023 Publication History

Abstract

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.

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    Published In

    cover image ACM Transactions on Multimedia Computing, Communications, and Applications
    ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 19, Issue 1s
    February 2023
    504 pages
    ISSN:1551-6857
    EISSN:1551-6865
    DOI:10.1145/3572859
    • Editor:
    • Abdulmotaleb El Saddik
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 03 February 2023
    Online AM: 29 April 2022
    Accepted: 17 April 2022
    Revised: 02 March 2022
    Received: 09 August 2021
    Published in TOMM Volume 19, Issue 1s

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    Author Tags

    1. Image restoration
    2. reinforcement learning
    3. deep learning

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    Funding Sources

    • Shenzhen Fundamental Research Program
    • Shenzhen Science and Technology Plan Basic Research
    • Guangdong Basic and Applied Basic Research Foundation
    • Natural Science Foundation of China

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