通过模拟对齐提升真实场景超分辨率训练效果
Enhanced Super-Resolution Training via Mimicked Alignment for Real-World Scenes
- 训练时生成与高分辨率图像对齐的低分辨率样本
- 在多个真实数据集上显著提升各类超分模型性能
- 模块可插即用且推理时无额外参数开销
图像超分辨率方法虽在深度学习和大量数据推动下取得进展，但在真实世界数据集中，低分辨率（LR）与高分辨率（HR）图像间存在固有偏移问题。本文提出一种新型即插即用模块，在训练过程中通过模拟一个与HR对齐的新型LR样本，同时保留原始LR的退化特征，以缓解该问题。该模块可无缝集成至任意超分辨率模型，增强对偏移的鲁棒性，且在推理阶段可轻松移除，不引入额外参数。我们在合成与真实世界数据集上进行了全面评估，结果表明该方法在多种超分辨率模型（包括传统CNN与先进Transformer）上均具有效性。代码将公开于https://github.com/omarAlezaby/Mimicked_Ali。
原文摘要 · Abstract (English)
Image super-resolution methods have made significant strides with deep learning techniques and ample training data. However, they face challenges due to inherent misalignment between low-resolution (LR) and high-resolution (HR) pairs in real-world datasets. In this study, we propose a novel plug-and-play module designed to mitigate these misalignment issues by aligning LR inputs with HR images during training. Specifically, our approach involves mimicking a novel LR sample that aligns with HR while preserving the degradation characteristics of the original LR samples. This module seamlessly integrates with any SR model, enhancing robustness against misalignment. Importantly, it can be easily removed during inference, therefore without introducing any parameters on the conventional SR models. We comprehensively evaluate our method on synthetic and real-world datasets, demonstrating its effectiveness across a spectrum of SR models, including traditional CNNs and state-of-the-art Transformers. The source codes will be publicly made available at https://github.com/omarAlezaby/Mimicked_Ali .
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