Details

Autor(en) / Beteiligte

• Tian, Chunwei
• Zheng, Menghua
• Jiao, Tiancai
• Zuo, Wangmeng
• Zhang, Yanning
• Lin, Chia-Wen

Titel

A self-supervised CNN for image watermark removal

Ist Teil von

• IEEE transactions on circuits and systems for video technology, 2024, p.1-1

Ort / Verlag

IEEE

Erscheinungsjahr

2024

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Popular convolutional neural networks mainly use paired images in a supervised way for image watermark removal. However, watermarked images do not have reference images in the real world, which results in poor robustness of image watermark removal techniques. In this paper, we propose a self-supervised convolutional neural network (CNN) in image watermark removal (SWCNN). SWCNN uses a self-supervised way to construct reference watermarked images rather than given paired training samples, according to watermark distribution. A heterogeneous U-Net architecture is used to extract more complementary structural information via simple components for image watermark removal. Taking into account texture information, a mixed loss is exploited to improve visual effects of image watermark removal. Besides, a watermark dataset is conducted. Experimental results show that the proposed SWCNN is superior to popular CNNs in image watermark removal.