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Yeah, this annoyed me about the paper. Clearly there are significant visual issues with patterns forming, but they just hand wave it away and basically say "according to all these common quality metrics, this method is superior". These patterns make it a non-starter for a ton of the use cases for blue noise, the authors should have been far more humble in making their case and addressing counterpoints.

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There are definitely cases where you want the frequency domain power spectrum to be as close to perfect as possible, and this paper (GBN) is a step above the previous SoTA (Blue Noise Through Optimal Transport).

However I agree about the visual result… Qualitatively, the GBN results look almost like WSJ illustrations, with tight, flowing patterns. In contrast, GBN looks noisier but much more organic, like film grain.

Whether the visual difference will matter depends on the application. If you're reducing grayscale to 1 bit, as in the paper's examples, you're very aware of any patterns. However if you're reducing a floating point color image to 8 bits per color channel, you may never be able to spot the difference.

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