用扩散模型统一提升哨兵2号12个波段的分辨率至2.5米。
DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion Models
- 用高分辨率RGB图像训练扩散模型，模拟哨兵2号特征。
- 融合网络利用超分后的RGB作为空间先验，提升多光谱波段。
- 在开源数据集上优于现有方法，尤其抑制了伪影生成。
本文提出DiffFuSR，一个模块化流程，将哨兵2号Level-2A影像的全部12个光谱波段统一超分辨至2.5米地面采样距离（GSD）。该流程分为两阶段：(i) 基于扩散模型的超分辨（SR）模型，使用来自NAIP和WorldStrat数据集的高分辨率RGB图像训练，经调和以模拟哨兵2号特性；(ii) 一个学习型融合网络，利用超分辨后的RGB图像作为空间先验，对剩余多光谱波段进行上采样。我们引入稳健的退化模型与对比退化编码器，支持盲超分辨。在OpenSR基准上的广泛评估表明，所提方法在反射率保真度、光谱一致性、空间对齐及伪影抑制方面均优于当前SOTA基线。此外，融合网络显著优于经典与学习型全色锐化方法，有效提升了哨兵2号20米和60米波段的精度。本工作提出一种新颖的模块化哨兵2号超分辨框架，结合调和学习与扩散模型及融合策略。代码与模型见https://github.com/NorskRegnesentral/DiffFuSR。
原文摘要 · Abstract (English)
This paper presents DiffFuSR, a modular pipeline for super-resolving all 12 spectral bands of Sentinel-2 Level-2A imagery to a unified ground sampling distance (GSD) of 2.5 meters. The pipeline comprises two stages: (i) a diffusion-based super-resolution (SR) model trained on high-resolution RGB imagery from the NAIP and WorldStrat datasets, harmonized to simulate Sentinel-2 characteristics; and (ii) a learned fusion network that upscales the remaining multispectral bands using the super-resolved RGB image as a spatial prior. We introduce a robust degradation model and contrastive degradation encoder to support blind SR. Extensive evaluations of the proposed SR pipeline on the OpenSR benchmark demonstrate that the proposed method outperforms current SOTA baselines in terms of reflectance fidelity, spectral consistency, spatial alignment, and hallucination suppression. Furthermore, the fusion network significantly outperforms classical and learned pansharpening approaches, enabling accurate enhancement of Sentinel-2's 20 m and 60 m bands. This work proposes a novel modular framework Sentinel-2 SR that utilizes harmonized learning with diffusion models and fusion strategies. Our code and models can be found at https://github.com/NorskRegnesentral/DiffFuSR.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。