Feature pyramid-based convolutional neural network image inpainting

Deep learning-based methods are widely used in the field of image processing and have achieved remarkable results. However, these methods often produce mis-filling phenomenon when dealing with irregular broken images. The main reason is that the underlying information of the feature map is not fully utilized, and the semantic information of feature maps at different scales cannot complement each other effectively. Therefore, we propose a network structure based on feature pyramid. In the first stage, we set the expansion factor used to avoid the grid effect and increase the receptive field, while maximizing the use of the underlying feature map information. The second stage uses a feature fusion branch, which first samples the feature maps to construct the feature pyramid, second fuses feature maps with different resolutions and semantic strengths, and finally, generates an image by back-convolution of the feature maps with a decoder. Our experimental results show that this method generates recovered regions with coherent, clear, and visually reasonable images, superior to other methods in terms of image quality.