arXiv:2505.05088v1 Announce Type: new
Abstract: Visible watermark removal is challenging due to its inherent complexities and the noise carried within images. Existing methods primarily rely on supervised learning approaches that require paired datasets of watermarked and watermark-free images, which are often impractical to obtain in real-world scenarios. To address this challenge, we propose SSH-Net, a Self-Supervised and Hybrid Network specifically designed for noisy image watermark removal. SSH-Net synthesizes reference watermark-free images using the watermark distribution in a self-supervised manner and adopts a dual-network design to address the task. The upper network, focused on the simpler task of noise removal, employs a lightweight CNN-based architecture, while the lower network, designed to handle the more complex task of simultaneously removing watermarks and noise, incorporates Transformer blocks to model long-range dependencies and capture intricate image features. To enhance the model’s effectiveness, a shared CNN-based feature encoder is introduced before dual networks to extract common features that both networks can leverage. Our code will be available at https://github.com/wenyang001/SSH-Net.
Expert Commentary: Self-Supervised and Hybrid Network for Noisy Image Watermark Removal
Visible watermark removal is a significant challenge in image processing due to the complexities inherent in the process and the noise that is often present in images. Existing methods for watermark removal typically rely on supervised learning approaches that require paired datasets of watermarked and watermark-free images. However, obtaining such datasets in real-world scenarios is often impractical.
In this context, the SSH-Net proposed in this study offers a novel solution to the problem of noisy image watermark removal. By synthesizing reference watermark-free images in a self-supervised manner, SSH-Net avoids the need for paired datasets. The network architecture consists of two components: an upper network focused on noise removal using a lightweight CNN-based design, and a lower network that tackles the more complex task of removing watermarks and noise simultaneously through the use of Transformer blocks.
One interesting aspect of the SSH-Net model is the incorporation of a shared CNN-based feature encoder before the dual networks. This feature encoder helps extract common features that are beneficial for both the noise removal and watermark removal tasks, enhancing the overall effectiveness of the model.
Multimedia information systems, animations, artificial reality, augmented reality, and virtual realities are all fields that could benefit from advancements in image processing techniques such as watermark removal. The multi-disciplinary nature of this study highlights the importance of integrating different approaches and technologies to address complex challenges in image processing.
Overall, the SSH-Net presents a promising approach to noisy image watermark removal that has the potential to offer practical solutions in real-world scenarios where paired datasets are not readily available.