用神经算子引导扩散模型，实现任意缩放的遥感图像超分辨率。
NeurOp-Diff:Continuous Remote Sensing Image Super-Resolution via Neural Operator Diffusion
- 用神经算子编码多尺度特征，作为扩散模型的先验条件。
- 支持任意缩放因子s，生成无伪影、不模糊的高质量图像。
- 适合需要连续分辨率适配的遥感图像处理场景。
现有公开遥感数据普遍分辨率低，限制了实际应用。为解决此问题，我们提出基于神经算子引导的扩散模型（NeurOp-Diff），实现连续遥感图像超分辨率。神经算子学习任意尺度下的分辨率表示，将低分辨率（LR）图像编码为高维特征，并作为先验指导扩散模型去噪。该方法有效缓解了传统超分辨率方法中存在的伪影和过度平滑问题，可生成高质量、连续的超分辨率图像。具体而言，通过调整缩放因子s，模型能自适应不同超分辨率倍数。在多个数据集上的实验验证了该方法的有效性。代码已开源：https://github.com/zerono000/NeurOp-Diff。
原文摘要 · Abstract (English)
Most publicly accessible remote sensing data suffer from low resolution, limiting their practical applications. To address this, we propose a diffusion model guided by neural operators for continuous remote sensing image super-resolution (NeurOp-Diff). Neural operators are used to learn resolution representations at arbitrary scales, encoding low-resolution (LR) images into high-dimensional features, which are then used as prior conditions to guide the diffusion model for denoising. This effectively addresses the artifacts and excessive smoothing issues present in existing super-resolution (SR) methods, enabling the generation of high-quality, continuous super-resolution images. Specifically, we adjust the super-resolution scale by a scaling factor s, allowing the model to adapt to different super-resolution magnifications. Furthermore, experiments on multiple datasets demonstrate the effectiveness of NeurOp-Diff. Our code is available at https://github.com/zerono000/NeurOp-Diff.
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