Image Inpainting using Partial Convolution
This addresses image restoration for computer vision applications, but appears incremental as it builds on existing deep learning approaches.
The paper tackles image inpainting by applying partial convolution layers in deep learning methods to restore images corrupted by noise or missing information, but it does not provide concrete numerical results.
Image Inpainting is one of the very popular tasks in the field of image processing with broad applications in computer vision. In various practical applications, images are often deteriorated by noise due to the presence of corrupted, lost, or undesirable information. There have been various restoration techniques used in the past with both classical and deep learning approaches for handling such issues. Some traditional methods include image restoration by filling gap pixels using the nearby known pixels or using the moving average over the same. The aim of this paper is to perform image inpainting using robust deep learning methods that use partial convolution layers.