MNet: A multi-scale network for visible watermark removal.

Superimposing visible watermarks on images is an efficient way to indicate ownership and prevent potential unauthorized use. Visible watermark removal technology is receiving increasing attention from researchers due to its ability to enhance the robustness of visible watermarks. In this paper, we propose MNet, a novel multi-scale network for visible watermark removal. In MNet, a variable number of simple U-Nets are stacked in each scale. There are two branches in MNet, i.e., the background restoration branch and the mask prediction branch. In the background restoration branch, we propose a different approach from current methods. Instead of directly reconstructing the background image, we pay great attention to predicting the anti-watermark image. In the watermark mask prediction branch, we adopt dice loss. This further supervises the predicted mask for better prediction accuracy. To make information flow more effective, we employ cross-layer feature fusion and intra-layer feature fusion among U-Nets. Moreover, a scale reduction module is employed to capture multi-scale information effectively. Our approach is evaluated on three different datasets, and the experimental results show that our approach achieves better performance than other state-of-the-art methods. Code will be available at https://github.com/Aitchson-Hwang/MNet.