【摘要】 遥感图像在成像链路中常受大气散射、传感器噪声及极端光照等多种异质退化因素的严重影响,现有一体化复原方法主要依赖隐式特征学习,缺乏对退化物理频域特性的显式建模及高阶语义交互能力。为此,本文提出一种结合频域物理感知与高阶语义融合的一体化遥感图像复原框架Aether。该框架构建频域自适应退化解析器(Harmonic-Adaptive Degradation Analyzer,HADA),利用数据驱动的可学习谐波滤波器组替代固定基底变换,实现对不同退化类型频谱指纹的自适应解析与精准提取。此外,设计基于Kolmogorov-Arnold表示定理的高阶非线性交互融合模块(Higher-Order Nonlinear Interaction Fusion,HONIF),通过样条函数网络构建高维映射空间,突破传统线性注意力的表达瓶颈,实现退化先验与图像特征的深度语义对齐。在MD-RSID、MD-RRSHID及MDRS-Landsat三个基准数据集上的实验表明,Aether在处理雾霾、噪声、模糊及低光等退化问题时均取得最优性能。特别是在MDRS-Landsat数据集上,该方法在去雾任务中的峰值信噪比(Peak Signal-to-Noise Ratio,PSNR)较次优方法提升3.63 dB,在低光增强任务中PSNR提升1.60 dB,且可学习感知图像块相似度(Learned Perceptual Image Patch Similarity,LPIPS)改善了75.2%,有效解决了复杂遥感场景下的一体化通用复原难题。更多还原

【Abstract】 Remote sensing imagery is often severely degraded along the imaging chain by heterogeneous factors such as atmospheric scattering, sensor noise, and extreme illumination conditions. Existing all-in-one restoration approaches predominantly rely on implicit feature learning, lacking explicit modeling of the physical frequency-domain properties of degradation and the ability to capture higher-order semantic interactions. To address these limitations, we present Aether, a unified remote sensing image restoration framework that integrates frequency-domain physical perception with higher-order semantic fusion. Aether introduces a harmonic-adaptive degradation analyzer(HADA), which replaces fixed-basis transforms with a data-driven learnable harmonic filter bank. This enables adaptive parsing and precise extraction of spectral fingerprints associated with diverse degradation types. In addition, we design a higher-order nonlinear interaction fusion module(HONIF) grounded in the Kolmogorov-Arnold representation theorem. HONIF constructs a high-dimensional mapping space via spline-based function networks, overcoming the representational bottleneck of conventional linear attention and facilitating deep semantic alignment between degradation priors and image features. Experiments on three benchmark datasets—MD-RSID, MD-RRSHID, and MDRS-Landsat—demonstrate that Aether achieves state-of-the-art performance across haze removal, denoising, deblurring, and low-light enhancement tasks. Notably, on the MDRS-Landsat dataset, Aether surpasses the second-best method by 3.63 dB in peak signal-to-noise ratio(PSNR) for dehazing and by 1.60 dB for low-light enhancement, while improving learned perceptual image patch similarity(LPIPS) by 75.2%, effectively addressing the longstanding challenge of unified and generalizable restoration in complex remote sensing scenarios.更多还原