标题： 增强的UGC图像超分辨率语义提取与引导 显示英文标题

标题： Enhanced Semantic Extraction and Guidance for UGC Image Super Resolution

摘要： 由于用户生成内容(UGC)图像中的真实退化与合成退化之间的差异，传统超分辨率方法难以有效泛化，需要一种更稳健的方法来建模真实世界的失真。 在本文中，我们通过将语义指导整合到扩散框架中，提出了一种新的UGC图像超分辨率方法。 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，解决了野生和合成数据集之间退化的不一致性。 此外，我们通过引入预训练的语义提取模型(SAM2)并微调关键超参数，以提高感知保真度，从而增强了退化去除和细节生成。 大量实验表明，我们的方法优于最先进的方法。 此外，所提出的模型在CVPR NTIRE 2025 短格式UGC图像超分辨率挑战赛中获得第二名，进一步验证了其有效性。 代码可在https://github.c10pom/Moonsofang/NTIRE-2025-SRlab获取。 显示英文摘要

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.