REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration

• URL: http://arxiv.org/abs/2207.12056v1
• Date: Mon, 25 Jul 2022 10:56:10 GMT
• Title: REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration
• Authors: Chong Wang, Rongkai Zhang, Saiprasad Ravishankar, Bihan Wen
• Abstract summary: We propose a novel deep reinforcement learning (DRL) based framework dubbed RePNP. Results demonstrate that the proposed RePNP is robust to the observation model used in the. scheme dubbed RePNP. RePNP achieves better results subjective to model deviation with fewer model parameters.
• Score: 30.966005373669027
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image restoration schemes based on the pre-trained deep models have received great attention due to their unique flexibility for solving various inverse problems. In particular, the Plug-and-Play (PnP) framework is a popular and powerful tool that can integrate an off-the-shelf deep denoiser for different image restoration tasks with known observation models. However, obtaining the observation model that exactly matches the actual one can be challenging in practice. Thus, the PnP schemes with conventional deep denoisers may fail to generate satisfying results in some real-world image restoration tasks. We argue that the robustness of the PnP framework is largely limited by using the off-the-shelf deep denoisers that are trained by deterministic optimization. To this end, we propose a novel deep reinforcement learning (DRL) based PnP framework, dubbed RePNP, by leveraging a light-weight DRL-based denoiser for robust image restoration tasks. Experimental results demonstrate that the proposed RePNP is robust to the observation model used in the PnP scheme deviating from the actual one. Thus, RePNP can generate more reliable restoration results for image deblurring and super resolution tasks. Compared with several state-of-the-art deep image restoration baselines, RePNP achieves better results subjective to model deviation with fewer model parameters.

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