The paper introduces Mi-Ripple, a specialized restoration workflow designed to mitigate "digital ripple" artifacts—specifically grid-like and granular textures—caused by iterative reference-conditioned AI image editing. To address these degradations without compromising original image structure, the authors propose a diagnosis-guided methodology that distinguishes between periodic lattice artifacts and content-entangled granular textures. The technical approach integrates three primary mechanisms: selective spectral notching for isolated periodic artifacts, structure-aware smoothing for complex textures, and cleaned-reference regeneration for reconstruction. By decoupling these degradation types, the system can apply low-distortion filtering where possible or perform visual reconstruction when filtering would otherwise remove legitimate image details. Empirical evaluations demonstrate the method's efficacy; in fourteen notch-only executions, the whole-image residual standard deviation was measured between 0.08 and 0.44 in CIELAB lightness units. Furthermore, when employing paired regeneration, the workflow reduced output debris density by 45% through reference cleaning. Ultimately, Mi-Ripple prioritizes visible image clarity and structural integrity over mere spectral score optimization, providing a robust framework for repairing images damaged by repetitive generative AI manipulation.