空间-光谱残差引导的扩散神经算子用于全色锐化
Spatial-Spectral Residuals Informed Diffusion Neural Operator for Pan-sharpening
用Galerkin型神经算子替代注意力去噪骨干网络，在扩散逆过程中显式注入像素级空间-光谱残差作为闭环引导，为图像融合与复原任务提供了一种兼顾效率与保真度的扩散采样框架。
原文依据 replaces the standard attention-based denoising backbone with a Galerkin-type neural operator
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论全色锐化是遥感领域一项基础性的图像预处理技术，旨在通过整合纹理丰富的全色（PAN）图像与配对低分辨率多光谱（LRMS）图像中的互补信息，生成空间与光谱信息均更丰富的高分辨率多光谱图像。尽管近期生成式扩散模型在融合质量上取得了令人瞩目的成果，但这些性能提升往往伴随着高昂的计算成本，使其在遥感应用中常见的资源受限场景下难以实用。本文提出一种基于神经算子架构的函数空间扩散模型，在实现有竞争力性能的同时具备良好的效率。具体而言，我们的框架用Galerkin型神经算子替代标准的基于注意力的去噪骨干网络，在保持优异表示能力的同时显著降低计算复杂度。此外，通过将像素级空间-光谱一致性残差显式地融入每个反向扩散步骤，我们的方法建立了一种细粒度的闭环引导机制，在整个生成过程中动态校准空间细节与光谱保真度。在多个基准数据集上的大量实验表明，我们的方法优于最优方法。
英文原文摘要 1366 字符
Pan-sharpening, a fundamental image preprocessing technique in remote sensing, aims to generate spatially and spectrally enriched multispectral imagery by integrating complementary information from texture-rich panchromatic (PAN) images and paired low-resolution multispectral (LRMS) counterparts. Although recent generative diffusion models have achieved impressive fusion quality, these performance gains often come with substantial computational costs, rendering them impractical for resource-constrained scenarios common in remote sensing applications. This work introduces a function-space diffusion model built upon a neural operator architecture that achieves compelling performance with promising efficiency. Specifically, our framework replaces the standard attention-based denoising backbone with a Galerkin-type neural operator, significantly reducing computational complexity while maintaining excellent representational capacity. Furthermore, by explicitly integrating pixel-wise spatial-spectral consistency residuals into each reverse diffusion step, our method establishes a fine-grained, closed-loop guidance mechanism that dynamically calibrates spatial details and spectral fidelity throughout the generation process. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of our approach over state-of-the-art methods.