For clarity: the final algorithm isn’t novel research — it’s inspired by watermark-removal implementations shared in the community, then adapted specifically to Gemini’s logo geometry and optimized for browser-side execution.

The main engineering challenge for me wasn’t detection quality, but finding a point that balanced:

• reliability on textured images • millisecond latency • small binary size • zero server-side processing

I also made the extension paid mostly as an experiment to understand pricing, payments, and distribution for small developer tools. Building the payment/licensing flow took more time than the algorithm itself.

If anyone is curious, I’m happy to share more details about the detection heuristics and fill strategy.