Visible watermarks, while instrumental in protecting image copyrights, frequently distort the underlying content, complicating tasks like scene interpretation and image editing. Visible watermark removal aims to eliminate the interference of watermarks and restore the background content. However, existing methods often implement watermark component removal and background restoration tasks within a singular branch, leading to residual watermarks in the predictions and ignoring cases where watermarks heavily obscure the background. To address these limitations, this study introduces the Removing Interference and Recovering Content Imaginatively (RIRCI) framework. RIRCI embodies a two-stage approach: the initial phase centers on discerning and segregating the watermark component, while the subsequent phase focuses on background content restoration. To achieve meticulous background restoration, our proposed model employs a dual-path network capable of fully exploring the intrinsic background information beneath semi-transparent watermarks and peripheral contextual information from unaffected regions. Moreover, a Global and Local Context Interaction module is built upon multi-layer perceptrons and bidirectional feature transformation for comprehensive representation modeling in the background restoration phase. The efficacy of our approach is empirically validated across two large-scale datasets, and our findings reveal a marked enhancement over existing watermark removal techniques.

翻译：可见水印虽有助于保护图像版权，却常扭曲底层内容，给场景理解与图像编辑等任务带来困难。可见水印去除旨在消除水印干扰并恢复背景内容。然而，现有方法通常将水印移除与背景恢复任务整合于单一分支中，导致预测结果中存在残留水印，且忽略了水印严重遮挡背景的情况。针对上述不足，本研究提出抑制干扰与想象性内容恢复（RIRCI）框架。RIRCI采用两阶段处理策略：初始阶段专注于水印成分的识别与分离，后续阶段则聚焦于背景内容恢复。为实现精细的背景重建，所提模型采用双路径网络，能够充分挖掘半透明水印下的内在背景信息及未受影响区域的周围上下文信息。此外，在背景恢复阶段，基于多层感知机与双向特征变换构建了全局与局部上下文交互模块，以实现全面的表征建模。通过两个大规模数据集的实证验证，本方法较现有水印去除技术表现出显著性能提升。