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Watermarking serves as a widely adopted approach to safeguard mediacopyright. In parallel, the research focus has extended to watermark removaltechniques, offering an adversarial means to enhance watermark robustness andfoster advancements in the watermarking field. Existing watermark removalmethods mainly rely on UNet with task-specific decoder branches--one forwatermark localization and the other for background image restoration. However,watermark localization and background restoration are not isolated tasks;precise watermark localization inherently implies regions necessitatingrestoration, and the background restoration process contributes to moreaccurate watermark localization. To holistically integrate information fromboth branches, we introduce an implicit joint learning paradigm. This empowersthe network to autonomously navigate the flow of information between implicitbranches through a gate mechanism. Furthermore, we employ cross-channelattention to facilitate local detail restoration and holistic structuralcomprehension, while harnessing nested structures to integrate multi-scaleinformation. Extensive experiments are conducted on various challengingbenchmarks to validate the effectiveness of our proposed method. The resultsdemonstrate our approach's remarkable superiority, surpassing existingstate-of-the-art methods by a large margin.

WMFormer++: Nested Transformer for Visible Watermark Removal via Implict Joint Learning