Removing Interference and Recovering Content Imaginatively for Visible Watermark Removal (2312.14383v1)
Abstract: Visible watermarks, while instrumental in protecting image copyrights, frequently distort the underlying content, complicating tasks like scene interpretation and image editing. Visible watermark removal aims to eliminate the interference of watermarks and restore the background content. However, existing methods often implement watermark component removal and background restoration tasks within a singular branch, leading to residual watermarks in the predictions and ignoring cases where watermarks heavily obscure the background. To address these limitations, this study introduces the Removing Interference and Recovering Content Imaginatively (RIRCI) framework. RIRCI embodies a two-stage approach: the initial phase centers on discerning and segregating the watermark component, while the subsequent phase focuses on background content restoration. To achieve meticulous background restoration, our proposed model employs a dual-path network capable of fully exploring the intrinsic background information beneath semi-transparent watermarks and peripheral contextual information from unaffected regions. Moreover, a Global and Local Context Interaction module is built upon multi-layer perceptrons and bidirectional feature transformation for comprehensive representation modeling in the background restoration phase. The efficacy of our approach is empirically validated across two large-scale datasets, and our findings reveal a marked enhancement over existing watermark removal techniques.
- Generative adversarial networks model for visible watermark removal. IET Image Processing, 13(10): 1783–1789.
- Large-scale visible watermark detection and removal with deep convolutional networks. In Pattern Recognition and Computer Vision: First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part III 1, 27–40. Springer.
- Fast fourier convolution. Advances in Neural Information Processing Systems, 33: 4479–4488.
- Split then refine: stacked attention-guided ResUNets for blind single image visible watermark removal. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, 1184–1192.
- Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, 248–255. Ieee.
- Incremental transformer structure enhanced image inpainting with masking positional encoding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 11358–11368.
- The pascal visual object classes challenge: A retrospective. International journal of computer vision, 111: 98–136.
- Blind visual motif removal from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 6858–6867.
- Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
- Mat: Mask-aware transformer for large hole image inpainting. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 10758–10768.
- Towards photo-realistic visible watermark removal with conditional generative adversarial networks. In Image and Graphics: 10th International Conference, ICIG 2019, Beijing, China, August 23–25, 2019, Proceedings, Part I 10, 345–356. Springer.
- Visible watermark removal via self-calibrated localization and background refinement. In Proceedings of the 29th ACM International Conference on Multimedia, 4426–4434.
- CoordFill: Efficient High-Resolution Image Inpainting via Parameterized Coordinate Querying. arXiv preprint arXiv:2303.08524.
- Wdnet: Watermark-decomposition network for visible watermark removal. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 3685–3693.
- Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32.
- Context encoders: Feature learning by inpainting. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2536–2544.
- Concurrent spatial and channel ‘squeeze & excitation’in fully convolutional networks. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I, 421–429. Springer.
- Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
- DENet: Disentangled Embedding Network for Visible Watermark Removal. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, 2411–2419.
- Resolution-robust large mask inpainting with fourier convolutions. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, 2149–2159.
- Maxim: Multi-axis mlp for image processing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5769–5780.
- Non-local neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 7794–7803.
- Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4): 600–612.