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Learning adaptive hyper-guidance via proxy-based bilevel optimization for image enhancement

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Abstract

In recent years, image enhancement based on deep network plays a vital role and has become the mainstream research. However, current approaches are generally limited to the manual embedding of auxiliary components (e.g., hyper-parameters, appended modules) to train the network; thus, they can often lack flexibility, adaptability, or even fail to achieve the optimal settings. Moreover, the straightforward learning-based architectures cannot adequately handle the complex latent image distributions in real-world scenarios. To partially address the above issues, in this work, a generic adaptive hyper-training scheme based on bilevel optimization is established. Specifically, we propose a completely new bilevel deep-unfolded strategy to collaboratively optimize the inner-level task-related hyper-guidance and the outer-level image reconstruction. The process can embed the differentiable proxy-based network with parameters to automatically learn the appended control mechanism. Instead of constructing the empirically manual interventions, our strategy can proactively learn to learn self-adaptive auxiliary modules. Extensive experiments demonstrate the superiority of our strategy to address different image enhancement tasks (i.e., image restoration, image rain removal and image haze removal) in terms of flexibility and effectiveness.

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Notes

  1. It is known that the simple bi-level optimization is just a specific bilevel optimization problem with only one variable [35] ?,.

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Authors and Affiliations

  1. School of Software Technology, Dalian University of Technology, Dalian, China

    Jiaxin Gao & Xiaokun Liu

  2. International School of Information Science and Engineering, Dalian University of Technology, Dalian, China

    Risheng Liu & Xin Fan

  3. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China

    Jiaxin Gao, Xiaokun Liu, Risheng Liu & Xin Fan

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  1. Jiaxin Gao
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  2. Xiaokun Liu
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Correspondence to Risheng Liu.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “Learning Adaptive Hyper-guidance via Proxy-based Bilevel Optimization for Image Restoration”.

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Gao, J., Liu, X., Liu, R. et al. Learning adaptive hyper-guidance via proxy-based bilevel optimization for image enhancement. Vis Comput 39, 1471–1484 (2023). https://doi.org/10.1007/s00371-022-02423-3

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