Most super-resolution (SR) models struggle with real-world low-resolution (LR) images. This issue arises because the degradation characteristics in the synthetic datasets differ from those in real-world LR images. Since SR models are trained on pairs of high-resolution (HR) and LR images generated by downsampling, they are optimized for simple degradation. However, real-world LR images contain complex degradation caused by factors such as the imaging process and JPEG compression. Due to these differences in degradation characteristics, most SR models perform poorly on real-world LR images. This study proposes a dataset generation method using undertrained image reconstruction models. These models have the property of reconstructing low-quality images with diverse degradation from input images. By leveraging this property, this study generates LR images with diverse degradation from HR images to construct the datasets. Fine-tuning pre-trained SR models on our generated datasets improves noise removal and blur reduction, enhancing performance on real-world LR images. Furthermore, an analysis of the datasets reveals that degradation diversity contributes to performance improvements, whereas color differences between HR and LR images may degrade performance. 11 pages, (11 figures and 2 tables)

翻译：大多数超分辨率模型在处理真实世界低分辨率图像时面临困难。这一问题源于合成数据集中的退化特性与真实世界低分辨率图像存在差异。由于超分辨率模型通过下采样生成的高分辨率-低分辨率图像对进行训练，其优化目标针对简单退化。然而，真实世界低分辨率图像包含成像过程与JPEG压缩等因素导致的复杂退化。受退化特性差异影响，多数超分辨率模型在真实世界低分辨率图像上表现欠佳。本研究提出一种基于欠训练图像重建模型的数据集生成方法。该类模型具备从输入图像重建具有多样化退化的低质量图像的特性。利用此特性，本研究从高分辨率图像生成具有多样化退化的低分辨率图像以构建数据集。基于生成数据集对预训练超分辨率模型进行微调，可有效提升去噪与去模糊能力，从而增强对真实世界低分辨率图像的处理性能。进一步的数据集分析表明：退化多样性有助于性能提升，而高分辨率与低分辨率图像间的色彩差异可能导致性能下降。全文11页（含11幅图与2张表格）