Mi-ripple reduces digital ripple artifacts in ai edited images
Mi-Ripple: Restoring Images Degraded by Iterative AI Editing
Computer Vision and Pattern Recognition
Summary
Images edited repeatedly by AI can develop distracting grid-like or grainy textures called digital ripple. The authors present Mi-Ripple, a method that carefully identifies and reduces these unwanted patterns while keeping the important parts of the image intact. They do this by separating the different types of artifact textures and using special filters and cleaning steps that remove the distortions without losing detail. This approach results in visibly cleaner images and measurable reductions in artifact levels.
What this means in practice
- •For photo editing software developers: Integrate Mi-Ripple to automatically remove digital ripple artifacts from AI-edited images, improving visual quality without damaging image details.$Commercial implications: It enables photo software companies to offer enhanced AI editing tools that produce cleaner images, differentiating their products in the market.
- •For visual content production teams: Use Mi-Ripple to clean up images degraded by multiple AI edits, ensuring higher-quality visuals for marketing or media projects.
Authors
Jiayin Chen, Yicheng Xu, Muting Wang
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.