# SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening

arXiv: https://arxiv.org/abs/2404.11537 · HTML: https://mlsanity.com/paper/2404.11537/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 17 Apr 2024.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Implicit Diffusion Models for Continuous Super-Resolution](https://mlsanity.com/paper/2303.16491/) (2023)
- [FreeU: Free Lunch in Diffusion U-Net](https://mlsanity.com/paper/2309.11497/) (2023)
- [Pansharpening via Detail Injection Based Convolutional Neural Networks](https://mlsanity.com/paper/1806.08898/) (2018)
- [Memory-augmented Deep Unfolding Network for Guided Image Super-resolution](https://mlsanity.com/paper/2203.04960/) (2022)
- [Video Adverse-Weather-Component Suppression Network via Weather Messenger and Adversarial Backpropagation](https://mlsanity.com/paper/2309.13700/) (2023)
- [U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image Fusion](https://mlsanity.com/paper/2212.06466/) (2022)
- [Genuine Knowledge from Practice: Diffusion Test-Time Adaptation for Video Adverse Weather Removal](https://mlsanity.com/paper/2403.07684/) (2024)

## Built on by (most cited first, 15 of 15)

- [MMAIF: Multi-task and Multi-degradation All-in-One for Image Fusion with Language Guidance](https://mlsanity.com/paper/2503.14944/) (2025)
- [Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey](https://mlsanity.com/paper/2505.23248/) (2025)
- [Taming Flow Matching with Unbalanced Optimal Transport into Fast Pansharpening](https://mlsanity.com/paper/2503.14975/) (2025)
- [PAN-Crafter: Learning Modality-Consistent Alignment for PAN-Sharpening](https://mlsanity.com/paper/2505.23367/) (2025)
- [Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables](https://mlsanity.com/paper/2503.23793/) (2025)
- [SWIFT: A General Sensitive Weight Identification Framework for Fast Sensor-Transfer Pansharpening](https://mlsanity.com/paper/2507.20311/) (2025)
- [Enpowering Your Pansharpening Models with Generalizability: Unified Distribution is All You Need](https://mlsanity.com/paper/2510.22217/) (2025)
- [Remote Sensing Image Dehazing: A Systematic Review of Progress, Challenges, and Prospects](https://mlsanity.com/paper/2603.20289/) (2026)
- [AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors](https://mlsanity.com/paper/2605.14341/) (2026)
- [CAT: A Conditional Adaptation Tailor for Efficient and Effective Instance-Specific Pansharpening on Real-World Data](https://mlsanity.com/paper/2504.10242/) (2025)
- [Progressive Alignment Degradation Learning for Pansharpening](https://mlsanity.com/paper/2506.20179/) (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)
- [DMAConv: Dual Mask-Adaptive Convolution for Remote Sensing Pansharpening](https://mlsanity.com/paper/2512.08331/) (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)

## Abstract (arXiv)

Pansharpening is a significant image fusion technique that merges the spatial content and spectral characteristics of remote sensing images to generate high-resolution multispectral images. Recently, denoising diffusion probabilistic models have been gradually applied to visual tasks, enhancing controllable image generation through low-rank adaptation (LoRA). In this paper, we introduce a spatial-spectral integrated diffusion model for the remote sensing pansharpening task, called SSDiff, which considers the pansharpening process as the fusion process of spatial and spectral components from the perspective of subspace decomposition. Specifically, SSDiff utilizes spatial and spectral branches to learn spatial details and spectral features separately, then employs a designed alternating projection fusion module (APFM) to accomplish the fusion. Furthermore, we propose a frequency modulation inter-branch module (FMIM) to modulate the frequency distribution between branches. The two components of SSDiff can perform favorably against the APFM when utilizing a LoRA-like branch-wise alternative fine-tuning method. It refines SSDiff to capture component-discriminating features more sufficiently. Finally, extensive experiments on four commonly used datasets, i.e., WorldView-3, WorldView-2, GaoFen-2, and QuickBird, demonstrate the superiority of SSDiff both visually and quantitatively. The code will be made open source after possible acceptance.