Paper page - Mi-Ripple: Restoring Images Degraded by Iterative AI Editing

Source: https://huggingface.co/papers/2609.11317

Abstract

Mi-Ripple reduces digital ripple artifacts in edited images by separating lattice artifacts from texture and applying targeted spectral filtering and reference cleaning.

Iterative reference-conditioned image editing can introduce grid-like and granular textures, commonly described asdigital ripple. We presentMi-Ripple, a diagnosis-guided restoration workflow that suppresses thisdigital ripplewhile protecting image structure.Mi-Rippleseparatesperiodic lattice artifactsfrom content-entangled granular texture, then combines selectivespectral notching,structure-aware smoothing, andcleaned-reference regeneration. This separation enables low-distortion filtering when artifacts are spectrally isolated and visual reconstruction when filtering would erase legitimate detail. Across fourteen notch-only executions, whole-image residual standard deviation is 0.08--0.44 inCIELAB lightnessunits. In a paired regeneration example, reference cleaning reduces output debris density by 45\%.Mi-Ripplelinks measurable artifact reduction to visibly cleaner generated images, rather than optimizing a spectral score alone.

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