DFCL: Dual-pathway fusion contrastive learning for blind single-image visible watermark removal

Digital image watermarking is a prevalent method for image copyright protection. As watermark embedding techniques evolve, research in copyright protection has increasingly extended into watermark removal. Recent advancements in deep learning and generative technologies have led to the development of public watermark removal solutions, addressing issues such as plagiarized, illegal, or outdated watermarks while driving significant improvements in robust watermark embedding. Traditional image restoration often requires the manual selection of watermark mask regions, while common blind visible watermark removal techniques struggle with watermark detection accuracy and post-removal visual quality. To address these challenges, this paper introduces a dual-pathway fusion contrastive learning approach for blind single-image visible watermark removal. We conduct dual-pathway training of the image and gradient map, enhancing high-frequency feature acquisition and the accuracy of watermark spatial positioning through feature fusion. Additionally, contrastive learning ensures that the results closely resemble the original watermark-free images while distancing themselves from watermark content, resulting in improved background visual quality. Importantly, our blind watermark removal algorithm does not require additional watermark images or mask regions. Extensive experiments on three challenging benchmark datasets demonstrate the effectiveness of our approach in overcoming the limitations of existing methods.