Mi-Ripple: Restoring Images Degraded by Iterative AI Editing
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Abstract (arXiv)
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.
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References · 6
- 2021Alias-Free Generative Adversarial Networkscited by 1,184
- 2022On the detection of synthetic images generated by diffusion modelscited by 283
- 2020Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributionscited by 280
- 2023Self-Consuming Generative Models Go MADcited by 250
- 2019Fourier Spectrum Discrepancies in Deep Network Generated Imagescited by 139
- 2026Banana100: Breaking NR-IQA Metrics by 100 Iterative Image Replications with Nano Banana Procited by 1
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