用欠训练模型生成真实退化图像，提升超分辨率在真实场景的性能
Undertrained Image Reconstruction for Realistic Degradation in Blind Image Super-Resolution
- 用欠训练重建模型生成含复杂退化的低质量图像
- 在自建数据集上微调后，去噪与去模糊效果显著提升
- 适合关注真实世界图像恢复的算法研究者
多数超分辨率（SR）模型在真实低分辨率（LR）图像上表现不佳，原因在于合成数据集中的退化特征与真实图像不一致。现有模型基于下采样生成HR-LR配对数据，仅针对简单退化优化，而真实图像受成像过程、JPEG压缩等因素影响，退化更复杂。本文提出一种基于欠训练图像重建模型的数据集生成方法：该模型能从高分辨率图像中重构出具有多样退化的低质量图像。利用此特性，构建包含复杂退化的数据集。将预训练的SR模型在该数据集上微调后，显著提升噪声去除和模糊抑制能力，改善真实场景下的表现。分析表明，退化多样性有助于性能提升，而高分辨与低分辨图像间色彩差异可能降低模型效果。
原文摘要 · Abstract (English)
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)
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