Towards Realistic Data Generation for Real-World Super-Resolution

2024-06-11

arXiv.org

AI Summary

Real-world image super-resolution (SR) suffers from a generalization bottleneck due to the mismatch between synthetic training data and authentic degradations. Existing degradation simulation or learning methods struggle to simultaneously achieve scalability, realism, and diversity. To address this, we propose RealDGen, an unsupervised framework built upon diffusion models that introduces a novel content-degradation disentanglement architecture. RealDGen generates large-scale, realistic, and diverse paired LR-HR training data using only unpaired real-world low- and high-resolution images—requiring neither paired supervision nor explicit degradation priors. It jointly integrates content extraction, prior-free degradation modeling, and reconstruction. Evaluated on multiple real-world SR benchmarks, models trained on RealDGen-synthesized data consistently outperform those trained on conventional datasets. The generated degradations comprehensively cover mixed noise, blur, and compression artifacts, achieving state-of-the-art fidelity and diversity in both visual quality and degradation coverage.

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
• Web Mining and Content Analysis: Web data generation and simulation
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Existing image super-resolution (SR) techniques often fail to generalize effectively in complex real-world settings due to the significant divergence between training data and practical scenarios. To address this challenge, previous efforts have either manually simulated intricate physical-based degradations or utilized learning-based techniques, yet these approaches remain inadequate for producing large-scale, realistic, and diverse data simultaneously. In this paper, we introduce a novel Realistic Decoupled Data Generator (RealDGen), an unsupervised learning data generation framework designed for real-world super-resolution. We meticulously develop content and degradation extraction strategies, which are integrated into a novel content-degradation decoupled diffusion model to create realistic low-resolution images from unpaired real LR and HR images. Extensive experiments demonstrate that RealDGen excels in generating large-scale, high-quality paired data that mirrors real-world degradations, significantly advancing the performance of popular SR models on various real-world benchmarks.

Problem

• Address generalization failure in super-resolution techniques.
• Generate realistic, diverse, large-scale super-resolution data.
• Improve super-resolution model performance on real-world benchmarks.

Innovation

• Unsupervised learning framework
• Content-degradation decoupled model
• Realistic low-resolution image generation