用户生成内容（UGC）图像超分辨率的增强语义提取与引导
Enhanced Semantic Extraction and Guidance for UGC Image Super Resolution
AI总结：
针对UGC图像真实与合成退化差异导致传统超分泛化差的问题，提出集成语义引导的扩散框架方法，通过结合LSDIR数据集退化模拟、SAM2语义提取及超参数微调提升效果，获CVPR NTIRE 2025挑战赛第二名。
摘要：
由于用户生成内容（UGC）图像中的真实世界退化与合成退化存在差异，传统超分辨率方法难以有效泛化，因此需要更稳健的方法来建模真实世界失真。本文提出一种将语义引导集成到扩散框架中的UGC图像超分辨率新方法。我们的方法通过在LSDIR数据集上分别模拟退化过程，并将其与官方配对训练集结合，解决了真实场景与合成数据集退化的不一致问题。此外，我们通过引入预训练语义提取模型（SAM2）并微调关键超参数以提升感知保真度，增强了退化去除和细节生成效果。大量实验表明，该方法优于现有最先进方法，且在CVPR NTIRE 2025短视频UGC图像超分辨率挑战赛中获第二名，验证了有效性。代码见https://github.c10pom/Moonsofang/NTIRE-2025-SRlab。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
