# U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image Fusion

arXiv: https://arxiv.org/abs/2212.06466 · HTML: https://mlsanity.com/paper/2212.06466/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2022.

## Built on by (most cited first, 19 of 19)

- [SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening](https://mlsanity.com/paper/2404.11537/) (2024)
- [All-weather Multi-Modality Image Fusion: Unified Framework and 100k Benchmark](https://mlsanity.com/paper/2402.02090/) (2024)
- [A General Adaptive Dual-level Weighting Mechanism for Remote Sensing Pansharpening](https://mlsanity.com/paper/2503.13214/) (2025)
- [Linearly-evolved Transformer for Pan-sharpening](https://mlsanity.com/paper/2404.12804/) (2024)
- [UAVD-Mamba: Deformable Token Fusion Vision Mamba for Multimodal UAV Detection](https://mlsanity.com/paper/2507.00849/) (2025)
- [SWIFT: A General Sensitive Weight Identification Framework for Fast Sensor-Transfer Pansharpening](https://mlsanity.com/paper/2507.20311/) (2025)
- [PIF-Net: Ill-Posed Prior Guided Multispectral and Hyperspectral Image Fusion via Invertible Mamba and Fusion-Aware LoRA](https://mlsanity.com/paper/2508.00453/) (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)
- [M3PD Dataset: Dual-view Photoplethysmography (PPG) Using Front-and-rear Cameras of Smartphones in Lab and Clinical Settings](https://mlsanity.com/paper/2511.02349/) (2025)
- [CAT: A Conditional Adaptation Tailor for Efficient and Effective Instance-Specific Pansharpening on Real-World Data](https://mlsanity.com/paper/2504.10242/) (2025)
- [SatFusion: A Unified Framework for Enhancing Remote Sensing Images via Multi-Frame and Multi-Source Images Fusion](https://mlsanity.com/paper/2510.07905/) (2025)
- [Robust Non-negative Proximal Gradient Algorithm for Inverse Problems](https://mlsanity.com/paper/2510.23362/) (2025)
- [MambaX: Image Super-Resolution with State Predictive Control](https://mlsanity.com/paper/2511.18028/) (2025)
- [HSSDCT: Factorized Spatial-Spectral Correlation for Hyperspectral Image Fusion](https://mlsanity.com/paper/2602.00490/) (2026)
- [Universal Pansharpening Model](https://mlsanity.com/paper/2603.03831/) (2026)
- [PAKAN: Pixel Adaptive Kolmogorov-Arnold Network Modules for Pansharpening](https://mlsanity.com/paper/2603.15109/) (2026)
- [CoFusion: Multispectral and Hyperspectral Image Fusion via Spectral Coordinate Attention](https://mlsanity.com/paper/2604.10584/) (2026)
- [RAFNet: Region-Aware Fusion Network for Pansharpening](https://mlsanity.com/paper/2605.02184/) (2026)
- [Adaptive 3D Convolution for Remote Sensing Image Fusion](https://mlsanity.com/paper/2605.09455/) (2026)

## Abstract (arXiv)

In image fusion tasks, images obtained from different sources exhibit distinct properties. Consequently, treating them uniformly with a single-branch network can lead to inadequate feature extraction. Additionally, numerous works have demonstrated that multi-scaled networks capture information more sufficiently than single-scaled models in pixel-level computer vision problems. Considering these factors, we propose U2Net, a spatial-spectral-integrated double U-shape network for image fusion. The U2Net utilizes a spatial U-Net and a spectral U-Net to extract spatial details and spectral characteristics, which allows for the discriminative and hierarchical learning of features from diverse images. In contrast to most previous works that merely employ concatenation to merge spatial and spectral information, this paper introduces a novel spatial-spectral integration structure called S2Block, which combines feature maps from different sources in a logical and effective way. We conduct a series of experiments on two image fusion tasks, including remote sensing pansharpening and hyperspectral image super-resolution (HISR). The U2Net outperforms representative state-of-the-art (SOTA) approaches in both quantitative and qualitative evaluations, demonstrating the superiority of our method. The code is available at https://github.com/PSRben/U2Net.