Mi-Ripple: Restoring Images Degraded by Iterative AI Editing
Mi-Ripple is a diagnosis-guided restoration workflow that removes digital ripple artifacts introduced by iterative AI image editing while preserving structure.
Iterative reference-conditioned image editing can introduce grid-like and granular textures known as digital ripple. Mi-Ripple separates periodic lattice artifacts from content-entangled granular texture, then applies selective spectral notching, structure-aware smoothing, and cleaned-reference regeneration. In fourteen notch-only executions, whole-image residual standard deviation was 0.08-0.44 in CIELAB lightness units, and reference cleaning reduced output debris density by 45% in a paired example.
- Separates periodic lattice artifacts from granular texture for targeted filtering
- Combines spectral notching with cleaned-reference regeneration to avoid erasing detail
- Reference cleaning cut debris density by 45% in a paired test
- Residual artifact deviation of 0.08-0.44 CIELAB units across trials
From the original article
1 min read at huggingface.co
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.
By Jiayin Chen, Yicheng Xu, Muting Wang · huggingface.co
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