  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.
