Watermark removal, frozen in TensorFlow amber

What it does Watermark-Removal is a specialized wrapper around generative image inpainting techniques. It takes a watermarked image, masks the marked region, and uses a pre-trained model to synthesize replacement pixels. The author claims the output is indistinguishable from the original unwatermarked ground truth, but the README provides only side-by-side screenshots as evidence.

The interesting bit Rather than training a new architecture from scratch, the project applies established academic models—Contextual Attention (CVPR 2018) and Gated Convolution (ICCV 2019)—to the narrow domain of watermark removal. It is essentially a practical, if dated, demo of how generative inpainting can be repurposed for digital cleanup tasks.

Key highlights

• Built on established deep inpainting research (Contextual Attention and Gated Convolution).
• Designed for offline Docker use, though it requires a manually downloaded checkpoint from Google Drive.
• Targets specific watermark styles such as istock.
• Licensed under CC BY-NC, restricting use to non-commercial projects.

Caveats

• Google Colab support is explicitly marked as broken in the README.
• The project relies on TensorFlow 1.15.0, which is long out of support.
• README offers no detail on training data, generalization, or quantitative metrics beyond before/after screenshots.

Verdict Worth a look if you want a quick, local demo of generative watermark removal and can tolerate legacy dependencies. Avoid if you need a maintained library, commercial license, or reproducible training pipeline.