# Perceptive self-supervised learning network for noisy image watermark removal

arXiv: https://arxiv.org/abs/2403.02211 · HTML: https://mlsanity.com/paper/2403.02211/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 49 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Learning Deep CNN Denoiser Prior for Image Restoration](https://mlsanity.com/paper/1704.03264/) (2017)
- [Deep Learning on Image Denoising: An overview](https://mlsanity.com/paper/1912.13171/) (2019)
- [REFIT: A Unified Watermark Removal Framework For Deep Learning Systems With Limited Data](https://mlsanity.com/paper/1911.07205/) (2019)
- [WDNet: Watermark-Decomposition Network for Visible Watermark Removal](https://mlsanity.com/paper/2012.07616/) (2020)
- [Split then Refine: Stacked Attention-guided ResUNets for Blind Single Image Visible Watermark Removal](https://mlsanity.com/paper/2012.07007/) (2020)
- [Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal](https://mlsanity.com/paper/2207.08178/) (2022)
- [DRD-Net: Detail-recovery Image Deraining via Context Aggregation Networks](https://mlsanity.com/paper/1908.10267/) (2019)
- [Asymmetric CNN for image super-resolution](https://mlsanity.com/paper/2103.13634/) (2021)
- [Generative Adversarial Networks for Image Super-Resolution: A Survey](https://mlsanity.com/paper/2204.13620/) (2022)
- [A heterogeneous group CNN for image super-resolution](https://mlsanity.com/paper/2209.12406/) (2022)
- [Towards Photo-Realistic Visible Watermark Removal with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1905.12845/) (2019)
- [Joint Demosaicking and Denoising Benefits from a Two-stage Training Strategy](https://mlsanity.com/paper/2009.06205/) (2020)

## Built on by (most cited first, 3 of 3)

- [Box-Free Model Watermarks Are Prone to Black-Box Removal Attacks](https://mlsanity.com/paper/2405.09863/) (2024)
- [SSH-Net: A Self-Supervised and Hybrid Network for Noisy Image Watermark Removal](https://mlsanity.com/paper/2505.05088/) (2025)
- [Spectral-Spatial Synergistic Guided Network for Hyperspectral Salient Object Detection](https://mlsanity.com/paper/2607.21032/) (2026)

## Abstract (arXiv)

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.