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• W4360822018 abstract "In the last few years, image inpainting methods based on deep learning models had shown obvious advantages compared with existing traditional methods. The former can better generate visually reasonable image structure and texture information. However, the existing premier convolutional neural networks methods usually causes the problems of excessive color difference and image texture loss and distortion phenomenon. The paper has proposed an effective image inpainting method using generative adversarial networks, which is composed of two mutually independent generative confrontation networks. Among them, the image repair network module aims to solve the problem of repairing the irregular missing areas of the image, and its generator is based on a partial convolutional network. The image optimization network module aims to solve the problem of local chromatic aberration in the repaired images, and its generator has based on deep residual networks. Through the synergy of the two network modules, the visual effect and image quality of the images has improved. The experimental results can show that the proposed method (RNON) performs better from comparisons of qualitative and quantitative evaluations with state-of-the-arts in image inpainting quality field." @default.
• W4360822018 created "2023-03-25" @default.
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• W4360822018 date "2023-03-25" @default.
• W4360822018 modified "2023-10-09" @default.
• W4360822018 title "RNON: image inpainting via repair network and optimization network" @default.
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• W4360822018 doi "https://doi.org/10.1007/s13042-023-01811-y" @default.
• W4360822018 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37360881" @default.
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