Restoring Real-World Images with an Internal Detail Enhancement Diffusion Model

2025-05-24

AI Summary

Real-world image degradations—such as scratches, fading, noise, and low resolution—cause severe detail loss and hinder object-level color control in restoration. Method: We propose Internal Image Detail Enhancement (IIDE), a novel approach that embeds latent-space degradation modeling and diffusion-based denoising guidance into a pre-trained Stable Diffusion model, enabling joint preservation of structural/texture fidelity and text-driven, object-level local coloring—without fine-tuning. Contribution/Results: By synergistically integrating generative priors with conditional control, IIDE achieves state-of-the-art performance both qualitatively and quantitatively (e.g., superior LPIPS and FID scores). Experiments demonstrate high-fidelity restoration coupled with professional-grade editability, establishing a new paradigm for restoring real-world degraded images.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Restoring real-world degraded images, such as old photographs or low-resolution images, presents a significant challenge due to the complex, mixed degradations they exhibit, such as scratches, color fading, and noise. Recent data-driven approaches have struggled with two main challenges: achieving high-fidelity restoration and providing object-level control over colorization. While diffusion models have shown promise in generating high-quality images with specific controls, they often fail to fully preserve image details during restoration. In this work, we propose an internal detail-preserving diffusion model for high-fidelity restoration of real-world degraded images. Our method utilizes a pre-trained Stable Diffusion model as a generative prior, eliminating the need to train a model from scratch. Central to our approach is the Internal Image Detail Enhancement (IIDE) technique, which directs the diffusion model to preserve essential structural and textural information while mitigating degradation effects. The process starts by mapping the input image into a latent space, where we inject the diffusion denoising process with degradation operations that simulate the effects of various degradation factors. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art models in both qualitative assessments and perceptual quantitative evaluations. Additionally, our approach supports text-guided restoration, enabling object-level colorization control that mimics the expertise of professional photo editing.

Problem

• Restoring real-world degraded images with complex mixed degradations
• Preserving essential image details during high-fidelity restoration
• Enabling object-level colorization control in image restoration

Innovation

• Uses pre-trained Stable Diffusion as prior
• Applies Internal Image Detail Enhancement
• Injects degradation in latent space