Euler-inspired Decoupling Neural Operator for Efficient Pansharpening

2026-04-14

AI Summary

This work addresses the spectral-spatial blurring and high computational cost inherent in conventional deep learning approaches to pansharpening of remote sensing images, which often stem from iterative sampling strategies. To overcome these limitations, the authors propose a frequency-domain continuous mapping framework that leverages Euler’s formula to transform features into polar coordinates. Within this framework, an Euler feature interaction layer is designed to explicitly model phase rotation in the frequency domain for geometric alignment, while implicitly learning spectral distributions to preserve spectral fidelity. This approach uniquely enables decoupled yet synergistic optimization of spatial and spectral modeling. Built upon linearly weighted phase rotation and a decoupled neural operator architecture, the method achieves state-of-the-art performance across three benchmark datasets, significantly outperforming existing heavyweight models while maintaining high reconstruction accuracy and markedly improving computational efficiency.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Matrix & Tensor Methods
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Pansharpening aims to synthesize high-resolution multispectral (HR-MS) images by fusing the spatial textures of panchromatic (PAN) images with the spectral information of low-resolution multispectral (LR-MS) images. While recent deep learning paradigms, especially diffusion-based operators, have pushed the performance boundaries, they often encounter spectral-spatial blurring and prohibitive computational costs due to their stochastic nature and iterative sampling. In this paper, we propose the Euler-inspired Decoupling Neural Operator (EDNO), a physics-inspired framework that redefines pansharpening as a continuous functional mapping in the frequency domain. Departing from conventional Cartesian feature processing, our EDNO leverages Euler's formula to transform features into a polar coordinate system, enabling a novel explicit-implicit interaction mechanism. Specifically, we develop the Euler Feature Interaction Layer (EFIL), which decouples the fusion task into two specialized modules: 1) Explicit Feature Interaction Module, utilizing a linear weighting scheme to simulate phase rotation for adaptive geometric alignment; and 2) Implicit Feature Interaction Module, employing a feed-forward network to model spectral distributions for superior color consistency. By operating in the frequency domain, EDNO inherently captures global receptive fields while maintaining discretization-invariance. Experimental results on the three datasets demonstrate that EDNO offers a superior efficiency-performance balance compared to heavyweight architectures.

Problem

• pansharpening
• spectral-spatial blurring
• computational cost
• diffusion-based operators
• high-resolution multispectral imaging

Innovation

• Euler-inspired
• Decoupling Neural Operator
• Frequency Domain
• Pansharpening
• Explicit-Implicit Interaction