  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.
