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Mi-Ripple: Restoring Images Degraded by Iterative AI Editing

First seen · 9/10/2026, 04:00 AMLatest activity · 9/10/2026, 04:00 AM

Repeated reference-conditioned image editing tends to accumulate grid-like artifacts and granular noise across generations, a degradation termed 'digital ripple.' Mi-Ripple introduces a diagnosis-guided restoration pipeline that decouples periodic lattice distortions from content-entangled textures. By combining selective spectral notching, structure-preserving smoothing, and cleaned-reference regeneration, the workflow achieves low-distortion filtering (CIELAB lightness residual standard deviation of 0.08–0.44) and cuts downstream debris density by 45%.

Event heat · last 24 hours

There are 4 persisted snapshots in the last 24 hours. Peak heat was 0 at 9/12, 20:00; latest heat is 0.

There are 4 persisted snapshots in the last 24 hours. Peak heat was 0 at 9/12, 20:00; latest heat is 0.10.509/12, 20:00, event heat 09/12, 23:00, event heat 09/13, 02:00, event heat 09/13, 05:00, event heat 024 hours agoNow
  1. 9/12, 20:00, event heat 0
  2. 9/12, 23:00, event heat 0
  3. 9/13, 02:00, event heat 0
  4. 9/13, 05:00, event heat 0

Reporting Timeline

  1. AggregatorHuggingFace Daily Papers9/10, 04:00 AMnot independentRepresentative
    Mi-Ripple: Restoring Images Degraded by Iterative AI Editing