Image inpainting is an interesting technique in computer vision and artificial intelligence for plausibly filling in blank areas of an image by referring to their surrounding areas. Although its performance has been improved significantly using diverse convolutional neural network (CNN)-based models, these models have difficulty filling in some erased areas due to the kernel size of the CNN. If the kernel size is too narrow for the blank area, the models cannot consider the entire surrounding area, only partial areas or none at all. This issue leads to typical problems of inpainting, such as pixel reconstruction failure and unintended filling. To alleviate this, in this paper, we propose a novel inpainting model called UFC-net that reinforces two components in U-net. The first component is the latent networks in the middle of U-net to consider the entire surrounding area. The second component is theHadamard identity skip connection to improve the attention of the inpainting model on the blank areas and reduce computational cost.We performed extensive comparisons with other inpainting models using the Places2 dataset to evaluate the effectiveness of the proposed scheme. We report some of the results.
Bibliographical noteFunding Information:
Funding Statement: This research was supported in part by NRF (National Research Foundation of Korea) Grant funded by the Korean Government (No. NRF-2020R1F1A1074885) and in part by the Brain Korea 21 FOUR Project in 2021.
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- Computer vision
- Generative adversarial nets
- Image inpainting
- Image processing
- Image restoration
ASJC Scopus subject areas
- Modelling and Simulation
- Mechanics of Materials
- Computer Science Applications
- Electrical and Electronic Engineering