Researchers introduce Mi-Ripple, a restoration technique that targets degradation artifacts that accumulate from repeated cycles of AI-based image editing. The method distinguishes between periodic grid-like artifacts and granular texture distortions, then applies targeted filtering, including spectral notching and structure-preserving smoothing, to remove each defect type. Testing shows substantial artifact reduction, with whole-image residual standard deviation of 0.08 to 0.44 in CIELAB lightness units, and a 45% decrease in output debris density when reference cleaning is used.
