SOURCE-LINKED INTELLIGENCE
Mi-Ripple: Restoring Images Degraded by Iterative AI Editing
Iterative reference-conditioned image editing can introduce grid-like and granular textures, commonly described as digital ripple. We present Mi-Ripple, a diagnosis-guided restoration workflow that suppresses this digital ripple while protecting image structure. Mi-Ripple separates periodic lattice artifacts from content-entangled granular texture, then combines selective spectral notching, structure-aware smoothing, and cleaned-reference regeneration. This separation enables low-distortion filtering when artifacts are spectrally isolated and visual reconstruction when filtering would erase le
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-10T09:46:42.000Z
First collected: 2026-09-20T19:02:05.452Z. This is not the publication date.