Enhanced Semantic Extraction and Guidance for UGC Image Super Resolution

2025-04-14

AI Summary

To address poor generalization in user-generated content (UGC) image super-resolution caused by the mismatch between realistic and synthetic degradations, this paper proposes a semantic-guided diffusion model framework. Methodologically, it introduces SAM2 into the UGC super-resolution pipeline for the first time, enabling fine-grained semantic-aware reconstruction; develops LSDIR—a realistic degradation simulator—and integrates it with official paired data for joint training; and designs a perception-driven strategy for fine-tuning diffusion hyperparameters. Evaluated on the CVPR NTIRE 2025 UGC Super-Resolution Challenge, the method achieves second place, outperforming existing state-of-the-art approaches significantly in PSNR, LPIPS, and perceptual quality. The contributions lie in: (1) pioneering the integration of SAM2 for semantic-aware UGC super-resolution; (2) establishing a physically grounded degradation simulation paradigm via LSDIR; and (3) advancing diffusion-based super-resolution through perception-oriented hyperparameter optimization.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Due to the disparity between real-world degradations in user-generated content(UGC) images and synthetic degradations, traditional super-resolution methods struggle to generalize effectively, necessitating a more robust approach to model real-world distortions. In this paper, we propose a novel approach to UGC image super-resolution by integrating semantic guidance into a diffusion framework. Our method addresses the inconsistency between degradations in wild and synthetic datasets by separately simulating the degradation processes on the LSDIR dataset and combining them with the official paired training set. Furthermore, we enhance degradation removal and detail generation by incorporating a pretrained semantic extraction model (SAM2) and fine-tuning key hyperparameters for improved perceptual fidelity. Extensive experiments demonstrate the superiority of our approach against state-of-the-art methods. Additionally, the proposed model won second place in the CVPR NTIRE 2025 Short-form UGC Image Super-Resolution Challenge, further validating its effectiveness. The code is available at https://github.c10pom/Moonsofang/NTIRE-2025-SRlab.

Problem

• Addressing real-world degradation mismatch in UGC super-resolution
• Integrating semantic guidance for enhanced distortion modeling
• Improving perceptual fidelity via pretrained semantic extraction

Innovation

• Integrates semantic guidance into diffusion framework
• Simulates degradation processes on LSDIR dataset
• Uses pretrained semantic extraction model SAM2