The challenge of complex contexts
Standard algorithms are prone to artifacts or unnatural fixes when watermarks cover areas with complex textures or varied colors.
Targeted solutions
- Mixed use of attention mechanisms: Enable contextual_attention module in config.yml to enhance semantic understanding
- Area mask optimization: Create a precise watermark area mask to guide the repair and avoid affecting other areas
- subchannel processing: Process RGB channels separately before compositing to enhance natural color transition
- post-processing enhancement: Smoothing results using filters like OpenCV's edgePreservingFilter
expert advice
For particularly complex cases, it is recommended to 1) manually label the approximate region using PS first; 2) adjust the kernel size of the gated convolution; and 3) consider a two-phase process (localization followed by repair).
This answer comes from the articleWatermark Removal: open source image watermark removal tool, picture watermark recovery original imageThe
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