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，随着互联网时代的持续发展，在社交媒体上分享图像已成为常见现象。在某些情况下，水印被用作图像所有权的保护手段；然而，更多时候，人们可能希望去除这些水印图像，以获取未被遮挡的原始图像。本文提出了一种基于深度学习的视觉水印去除技术。受U型结构强大图像翻译性能的启发，我们提出了一种名为AdvancedUnet的端到端深度神经网络模型，该模型可同时提取并去除视觉水印。此外，我们嵌入了高效的RSU模块，以取代UNet中常用的残差块，从而在不显著增加计算成本的前提下提升整体架构的深度。深度监督混合损失函数以多尺度、三级层次结构引导网络学习输入图像与真实图像之间的变换。对比实验验证了我们方法的有效性。