Generative image inpainting via edge structure and color aware fusion |
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| Affiliation: | 1. School of Artificial Intelligence, University of Chinese Academy of Sciences, China;2. NLPR & CEBSIT & CRIPAC, CASIA, China;3. Visual Intelligence Department, Meituan, China;4. University of Science and Technology of China, China;1. Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China;2. Shenzhen Institute of Artificial Intelligence and Robotic for Society, Shenzhen 518000, China;1. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China;2. Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China |
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| Abstract: | Very recently, with the widespread research of deep learning, its achievements are increasingly evident in image inpainting tasks. However, many existing methods fail to effectively reconstruct vivid contents and refine structures. In order to solve this issue, in this paper, a novel two-stage generative adversarial network based on the fusion of edge structures and color aware maps is proposed. In the first-stage network, edges with missing regions are employed to train an edge structure generator. Meanwhile, the input image with missing regions is transformed into a global color feature map after the content aware fill algorithm and a large kernel size Gaussian filtering. In the second-stage network, the image fused from the edge map and the color map is used as a label to guide the network to reconstruct the refined image. Qualitative and quantitative experiments conducted on multiple public datasets demonstrate that the method proposed in this paper has superior performance. |
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| Keywords: | Deep learning Image inpainting Generative adversarial network Content aware fill Multi-map fusion |
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