Perceptive self-supervised learning network for noisy image watermark
  removal
Chunwei Tian Menghua Zheng Bo Li Yanning Zhang Shichao Zhang David Zhang
Popular methods usually use a degradation model in a supervised way to learn
a watermark removal model. However, it is true that reference images are
difficult to obtain in the real world, as well as collected images by cameras
suffer from noise. To overcome these drawbacks, we propose a perceptive
self-supervised learning network for noisy image watermark removal (PSLNet) in
this paper. PSLNet depends on a parallel network to remove noise and
watermarks. The upper network uses task decomposition ideas to remove noise and
watermarks in sequence. The lower network utilizes the degradation model idea
to simultaneously remove noise and watermarks. Specifically, mentioned paired
watermark images are obtained in a self supervised way, and paired noisy images
(i.e., noisy and reference images) are obtained in a supervised way. To enhance
the clarity of obtained images, interacting two sub-networks and fusing
obtained clean images are used to improve the effects of image watermark
removal in terms of structural information and pixel enhancement. Taking into
texture information account, a mixed loss uses obtained images and features to
achieve a robust model of noisy image watermark removal. Comprehensive
experiments show that our proposed method is very effective in comparison with
popular convolutional neural networks (CNNs) for noisy image watermark removal.
Codes can be obtained at https://github.com/hellloxiaotian/PSLNet.