Mi-Ripple: Restoring Images Degraded by Iterative AI Editing

2026-09-10

AI Summary

Mi-Ripple通过诊断引导的工作流程，使用选择性频谱抑制、结构感知平滑等方法，有效解决了迭代AI编辑导致的图像数字波纹问题。

Abstract

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 legitimate detail. Across fourteen notch-only executions, whole-image residual standard deviation is 0.08--0.44 in CIELAB lightness units. In a paired regeneration example, reference cleaning reduces output debris density by 45\%. Mi-Ripple links measurable artifact reduction to visibly cleaner generated images, rather than optimizing a spectral score alone.

Problem

• Iterative AI Editing
• Digital Ripple
• Image Degradation
• Grid-like Texture
• Granular Texture

Innovation

• diagnosis-guided restoration
• selective spectral notching
• structure-aware smoothing
• cleaned-reference regeneration