Euler-inspired Decoupling Neural Operator for Efficient Pansharpening
Summary: arXiv:2604.12463v1 Announce Type: cross
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.
Key Features of EDNO
The EDNO framework integrates several innovative components aimed at enhancing the efficiency and effectiveness of pansharpening:
- Euler Feature Interaction Layer (EFIL): This layer decouples the fusion task into two specialized modules:
- Explicit Feature Interaction Module: Utilizes a linear weighting scheme to simulate phase rotation for adaptive geometric alignment.
- Implicit Feature Interaction Module: Employs a feed-forward network to model spectral distributions for superior color consistency.
- Frequency Domain Operation: By operating in the frequency domain, EDNO inherently captures global receptive fields while maintaining discretization-invariance, which is critical for effective image synthesis.
Advantages of EDNO
Through experimental results on three distinct datasets, EDNO demonstrates several advantages over traditional methods:
- Efficiency: The innovative design allows for reduced computational costs without sacrificing image quality.
- Performance: EDNO surpasses heavyweight architectures, offering improved results in terms of both spectral accuracy and spatial fidelity.
- Flexibility: The decoupled nature of the framework allows for tailored adaptations in various applications of image processing.
Conclusion
The introduction of the Euler-inspired Decoupling Neural Operator marks a significant advancement in the field of pansharpening. By redefining the problem space and leveraging fundamental mathematical principles, EDNO sets a new benchmark for efficiency and performance in high-resolution multispectral image synthesis. As deep learning continues to evolve, frameworks like EDNO will pave the way for innovative solutions in image processing and beyond.