In recent years as the internet age continues to grow, sharing images on social media has become a common occurrence. In certain cases, watermarks are used as protection for the ownership of the image, however, in more cases, one may wish to remove these watermark images to get the original image without obscuring. In this work, we proposed a deep learning method based technique for visual watermark removal. Inspired by the strong image translation performance of the U-structure, an end-to-end deep neural network model named AdvancedUnet is proposed to extract and remove the visual watermark simultaneously. On the other hand, we embed some effective RSU module instead of the common residual block used in UNet, which increases the depth of the whole architecture without significantly increasing the computational cost. The deep-supervised hybrid loss guides the network to learn the transformation between the input image and the ground truth in a multi-scale and three-level hierarchy. Comparison experiments demonstrate the effectiveness of our method.

翻译：近年来,随着互联网时代的不断增长,在社交媒体上分享图像已成为一种常见现象。在某些情况下,水印被用作图像所有权的保护手段,然而,在更多情况下,人们不妨删除这些水印图像,以获得原始图像,而不掩盖原始图像。在这项工作中,我们提出了基于视觉水印清除的深层次学习方法。在U结构的强烈图像转换性能的启发下,建议同时提取和删除视觉水印。另一方面,我们嵌入一些有效的RSU模块,而不是UNet中使用的共同残余块,这样可以增加整个结构的深度,而不会大大增加计算成本。深超的混合损失引导网络在多层次和三个层次的层次上学习输入图像与地面真理之间的转变。比较实验显示了我们的方法的有效性。