# Wavelet-Assisted Multi-Frequency Attention Network for Pansharpening

arXiv: https://arxiv.org/abs/2502.04903 · HTML: https://mlsanity.com/paper/2502.04903/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2025.

## Built on by (most cited first, 13 of 13)

- [A General Adaptive Dual-level Weighting Mechanism for Remote Sensing Pansharpening](https://mlsanity.com/paper/2503.13214/) (2025)
- [Pansharpening for Thin-Cloud Contaminated Remote Sensing Images: A Unified Framework and Benchmark Dataset](https://mlsanity.com/paper/2603.14952/) (2026)
- [SWIFT: A General Sensitive Weight Identification Framework for Fast Sensor-Transfer Pansharpening](https://mlsanity.com/paper/2507.20311/) (2025)
- [Training and Inference within 1 Second -- Tackle Cross-Sensor Degradation of Real-World Pansharpening with Efficient Residual Feature Tailoring](https://mlsanity.com/paper/2508.07369/) (2025)
- [G-ZAP: A Generalizable Zero-Shot Framework for Arbitrary-Scale Pansharpening](https://mlsanity.com/paper/2603.14412/) (2026)
- [CAT: A Conditional Adaptation Tailor for Efficient and Effective Instance-Specific Pansharpening on Real-World Data](https://mlsanity.com/paper/2504.10242/) (2025)
- [Robust Non-negative Proximal Gradient Algorithm for Inverse Problems](https://mlsanity.com/paper/2510.23362/) (2025)
- [CC-Pan: Channel-wise Compression based Diffusion for Efficient Pan-Sharpening](https://mlsanity.com/paper/2602.04473/) (2026)
- [Universal Pansharpening Model](https://mlsanity.com/paper/2603.03831/) (2026)
- [Fast Model-guided Instance-wise Adaptation Framework for Real-world Pansharpening with Fidelity Constraints](https://mlsanity.com/paper/2604.08903/) (2026)
- [Semi-Supervised Flow Matching for Mosaiced and Panchromatic Fusion Imaging](https://mlsanity.com/paper/2604.20128/) (2026)
- [CGFformer: Cluster-Guidance Frequency Transformer for Pansharpening](https://mlsanity.com/paper/2605.01490/) (2026)
- [RAFNet: Region-Aware Fusion Network for Pansharpening](https://mlsanity.com/paper/2605.02184/) (2026)

## Abstract (arXiv)

Pansharpening aims to combine a high-resolution panchromatic (PAN) image with a low-resolution multispectral (LRMS) image to produce a high-resolution multispectral (HRMS) image. Although pansharpening in the frequency domain offers clear advantages, most existing methods either continue to operate solely in the spatial domain or fail to fully exploit the benefits of the frequency domain. To address this issue, we innovatively propose Multi-Frequency Fusion Attention (MFFA), which leverages wavelet transforms to cleanly separate frequencies and enable lossless reconstruction across different frequency domains. Then, we generate Frequency-Query, Spatial-Key, and Fusion-Value based on the physical meanings represented by different features, which enables a more effective capture of specific information in the frequency domain. Additionally, we focus on the preservation of frequency features across different operations. On a broader level, our network employs a wavelet pyramid to progressively fuse information across multiple scales. Compared to previous frequency domain approaches, our network better prevents confusion and loss of different frequency features during the fusion process. Quantitative and qualitative experiments on multiple datasets demonstrate that our method outperforms existing approaches and shows significant generalization capabilities for real-world scenarios.