Masked Face Inpainting Through Residual Attention UNet
This addresses the challenge of face mask inpainting for image processing applications, but it is incremental as it builds on existing UNet architectures with added attention and residual components.
The paper tackled the problem of realistic face mask removal in images by proposing a blind inpainting method using a residual attention UNet, which achieved high-fidelity restoration with fine details and minimized the gap to ground truth structures.
Realistic image restoration with high texture areas such as removing face masks is challenging. The state-of-the-art deep learning-based methods fail to guarantee high-fidelity, cause training instability due to vanishing gradient problems (e.g., weights are updated slightly in initial layers) and spatial information loss. They also depend on intermediary stage such as segmentation meaning require external mask. This paper proposes a blind mask face inpainting method using residual attention UNet to remove the face mask and restore the face with fine details while minimizing the gap with the ground truth face structure. A residual block feeds info to the next layer and directly into the layers about two hops away to solve the gradient vanishing problem. Besides, the attention unit helps the model focus on the relevant mask region, reducing resources and making the model faster. Extensive experiments on the publicly available CelebA dataset show the feasibility and robustness of our proposed model. Code is available at \url{https://github.com/mdhosen/Mask-Face-Inpainting-Using-Residual-Attention-Unet}