Abstract Inpainting high-resolution images with large holes challenges existing deep learning-based image inpainting methods. We present a novel framework—PyramidFill for inpainting, which explicitly disentangles the task into two sub-tasks: content completion and texture synthesis. PyramidFill attempts to complete of unknown regions in lower-resolution image, synthesize textures higher-resolut...

Most of the existing deep learning-based image super-resolution methods require a large number datasets or ground truth. However, these are not suitable for restoration real with different domains. Recently, Deep Image Prior (DIP) based on single-image explores prior and uses network structure as implicit to recover images, but it ignores explicit information actual itself. The addition can eff...

Super-resolution aims to produce a high-resolution image from a set of one or more low-resolution images by recovering or inventing plausible high-frequency image content. Typical approaches try to reconstruct a high-resolution image using the sub-pixel displacements of several lowresolution images, usually regularized by a generic smoothness prior over the high-resolution image space. Other me...