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Updated: Jun 5, 2025

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MNet: A multi-scale network for visible watermark removal.

Wenhong Huang¹, Yunshu Dai¹, Jianwei Fei¹

¹School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.

Neural Networks : the Official Journal of the International Neural Network Society

|December 8, 2024

Summary

This summary is machine-generated.

This study introduces MNet, a novel multi-scale network for visible watermark removal. MNet effectively predicts anti-watermark images and utilizes dice loss for accurate mask prediction, outperforming existing methods.

Keywords:

Deep neural networks Multi-scale network Multi-task learning Visible watermark removal Watermark

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Area of Science:

Computer Vision
Image Processing
Deep Learning

Background:

Visible watermarks are crucial for image ownership and preventing misuse.
Visible watermark removal technology is vital for enhancing watermark robustness.

Purpose of the Study:

To propose MNet, a novel multi-scale network for effective visible watermark removal.
To improve upon existing methods by focusing on anti-watermark image prediction.

Main Methods:

MNet employs stacked U-Nets across multiple scales.
It features a background restoration branch predicting anti-watermark images and a mask prediction branch using dice loss.
Cross-layer and intra-layer feature fusion enhance information flow, complemented by a scale reduction module for multi-scale information capture.

Main Results:

MNet demonstrated superior performance compared to state-of-the-art methods.
Evaluated on three diverse datasets, the approach achieved significant improvements in visible watermark removal.

Conclusions:

MNet offers an effective and robust solution for visible watermark removal.
The proposed architecture and techniques advance the field of image watermarking and content protection.