Blind Visual Motif Removal from a Single Image
This addresses the need for automated image cleaning in web and social media contexts, though it is an incremental improvement in blind removal techniques.
The paper tackles the problem of automatically removing overlaid visual motifs like text or symbols from a single image without knowing their location or geometry, achieving state-of-the-art results for both opaque and semi-transparent motifs.
Many images shared over the web include overlaid objects, or visual motifs, such as text, symbols or drawings, which add a description or decoration to the image. For example, decorative text that specifies where the image was taken, repeatedly appears across a variety of different images. Often, the reoccurring visual motif, is semantically similar, yet, differs in location, style and content (e.g. text placement, font and letters). This work proposes a deep learning based technique for blind removal of such objects. In the blind setting, the location and exact geometry of the motif are unknown. Our approach simultaneously estimates which pixels contain the visual motif, and synthesizes the underlying latent image. It is applied to a single input image, without any user assistance in specifying the location of the motif, achieving state-of-the-art results for blind removal of both opaque and semi-transparent visual motifs.