SCoRe: Clean Image Generation from Diffusion Models Trained on Noisy Images

2026-04-10

AI Summary

Diffusion models trained on noisy data often produce images with high-frequency artifacts that degrade visual quality. To address this, this work proposes SCoRe, a training-free inference-time method that combines spectral truncation with SDEdit to suppress noise-induced artifacts and regenerate high-frequency details. The key innovation lies in establishing, for the first time, a theoretical mapping between the spectral truncation frequency and the SDEdit initialization timestep, enabling effective noise suppression without retraining. Guided by radial average power spectral density (RAPSD) analysis, SCoRe achieves high-quality image restoration through controlled attenuation and regeneration of high-frequency components. Experiments demonstrate that SCoRe significantly outperforms existing post-processing and noise-robust baselines on CIFAR-10 and SIDD, yielding generations that closely align with the distribution of clean data.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Diffusion models trained on noisy datasets often reproduce high-frequency training artifacts, significantly degrading generation quality. To address this, we propose SCoRe (Spectral Cutoff Regeneration), a training-free, generation-time spectral regeneration method for clean image generation from diffusion models trained on noisy images. Leveraging the spectral bias of diffusion models, which infer high-frequency details from low-frequency cues, SCoRe suppresses corrupted high-frequency components of a generated image via a frequency cutoff and regenerates them via SDEdit. Crucially, we derive a theoretical mapping between the cutoff frequency and the SDEdit initialization timestep based on Radially Averaged Power Spectral Density (RAPSD), which prevents excessive noise injection during regeneration. Experiments on synthetic (CIFAR-10) and real-world (SIDD) noisy datasets demonstrate that SCoRe substantially outperforms post-processing and noise-robust baselines, restoring samples closer to clean image distributions without any retraining or fine-tuning.

Problem

• diffusion models
• noisy images
• high-frequency artifacts
• image generation
• spectral bias

Innovation

• Spectral Cutoff Regeneration
• Diffusion Models
• Frequency-domain Denoising
• SDEdit
• Radially Averaged Power Spectral Density