Background
When using the Watermark Removal tool to remove image watermarks, you may experience incomplete watermark removal or unsatisfactory repair results. This is usually related to model selection, parameter settings, or preprocessing steps.
Core Solutions
- Choosing the right type of watermark: specify the correct -watermark_type parameter when running the script, the tool optimizes the model for different types of watermarks
- Adjusting the iteration parameters: you can increase the value of the -max_iter parameter in main.py to increase the granularity of the repair (but increase the processing time)
- Preprocessing of input images: It is recommended to first resize the image (512 x 512 pixels is recommended) and center the watermark area for best results.
- Mixed use of restoration techniquesFor complex watermarks, you can use a combination of Gated Convolution and Contextual Attention techniques provided in the project.
advanced skill
For professional users, consider 1) retraining the model using your own dataset; 2) adjusting the attention mechanism parameter in model/config.yml; and 3) experimenting with different combinations of loss function weights.
This answer comes from the articleWatermark Removal: open source image watermark removal tool, picture watermark recovery original imageThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of incomplete removal of image watermark and improve the restoration effect?
