Abstract
Image Super-Resolution (SR) aims to recover high-resolution (HR) details from low-resolution (LR) inputs, a task where Denoising Diffusion Probabilistic Models (DDPMs) have recently shown superior performance compared to Generative Adversarial Networks (GANs) based approaches. However, standard diffusion-based SR models, such as SR3, are typically trained on fixed-size patches and struggle to scale to arbitrary-sized images due to memory constraints. Applying these models via independent patch processing leads to visible seams and inconsistent textures across boundaries. In this paper, we propose InfScene-SR, a framework enabling spatially continuous super-resolution for large, arbitrary scenes. We adapt the iterative refinement process of diffusion models with a novel guided and variance-corrected fusion mechanism, allowing for the seamless generation of large-scale high-resolution imagery without retraining. We validate our approach on remote sensing datasets, demonstrating that InfScene-SR not only reconstructs fine details with high perceptual quality but also eliminates boundary artifacts, benefiting downstream tasks such as semantic segmentation.
Abstract (translated)
图像超分辨率（SR）旨在从低分辨率（LR）输入中恢复高分辨率（HR）细节，这一任务最近表明，去噪扩散概率模型（DDPMs）的性能优于基于生成对抗网络（GANs）的方法。然而，标准的基于扩散的超分辨率模型（如SR3）通常是在固定大小的图像块上进行训练的，在面对任意尺寸图像时会由于内存限制而难以扩展。通过独立处理这些图像块会导致可见的接缝和跨边界不一致的纹理问题。在本文中，我们提出了InfScene-SR框架，该框架能够实现大范围、任意场景的空间连续超分辨率生成。我们通过对扩散模型的迭代细化过程进行适应，并引入了一种新的引导式且方差校正融合机制，使得无需重新训练即可无缝地生成大规模高分辨率图像。我们在遥感数据集上验证了这种方法的有效性，证明InfScene-SR不仅能够以高度感知质量重建细节，还消除了边界伪影，从而有利于下游任务如语义分割。
URL
https://arxiv.org/abs/2602.19736
