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• W3119222877 abstract "This paper presents a novel multi-level generative chaotic Recurrent Neural Network (RNN) for image inpainting. This technique utilizes a general framework with multiple chaotic RNN that makes learning the image prior from a single corrupted image more robust and efficient. The proposed network utilizes a randomly-initialized process for parameterization, along with a unique quad-directional encoder structure, chaotic state transition, and adaptive importance for multi-level RNN updating. The efficacy of the approach has been validated through multiple experiments. In spite of a much lower computational load, quantitative comparisons reveal that the proposed approach exceeds the performance of several image-restoration benchmarks." @default.
• W3119222877 created "2021-01-18" @default.
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• W3119222877 date "2021-01-01" @default.
• W3119222877 modified "2023-10-16" @default.
• W3119222877 title "Multi-Level Generative Chaotic Recurrent Network for Image Inpainting" @default.
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• W3119222877 doi "https://doi.org/10.1109/wacv48630.2021.00367" @default.
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