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• W4312097551 abstract "It is known notoriously that most deep learning-based computer vision models are black-boxes. In this paper, a preliminary attempt has been made, deriving an interpretable representation learning approach to a specific inverse imaging problem, i.e., image inpainting. The new approach is not only rooted in the classical optimization framework, but also closely connected to the numerical differential equations. What is more important, numerous experimental results demonstrate that the proposed approach has achieved comparable or even better performance than many cutting-edge restoration algorithms, particularly those based on either sparse representation or naïve deep representation." @default.
• W4312097551 created "2023-01-04" @default.
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• W4312097551 date "2022-11-05" @default.
• W4312097551 modified "2023-10-17" @default.
• W4312097551 title "An Iterative Majorization-Minimization Approach to Deep Denoiser-Plugged Image Inpainting" @default.
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• W4312097551 doi "https://doi.org/10.1109/cisp-bmei56279.2022.9980117" @default.
• W4312097551 hasPublicationYear "2022" @default.
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