# Pansharpening via Detail Injection Based Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1806.08898 · HTML: https://mlsanity.com/paper/1806.08898/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2018.

## Built on by (most cited first, 29 of 29)

- [Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review](https://mlsanity.com/paper/2205.01380/) (2022)
- [Deep Gradient Projection Networks for Pan-sharpening](https://mlsanity.com/paper/2103.04584/) (2021)
- [Content-Adaptive Non-Local Convolution for Remote Sensing Pansharpening](https://mlsanity.com/paper/2404.07543/) (2024)
- [SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening](https://mlsanity.com/paper/2404.11537/) (2024)
- [Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity](https://mlsanity.com/paper/2307.14403/) (2023)
- [A General Adaptive Dual-level Weighting Mechanism for Remote Sensing Pansharpening](https://mlsanity.com/paper/2503.13214/) (2025)
- [A Triple-Double Convolutional Neural Network for Panchromatic Sharpening](https://mlsanity.com/paper/2112.02237/) (2021)
- [DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion](https://mlsanity.com/paper/2304.04774/) (2023)
- [Taming Flow Matching with Unbalanced Optimal Transport into Fast Pansharpening](https://mlsanity.com/paper/2503.14975/) (2025)
- [Proximal PanNet: A Model-Based Deep Network for Pansharpening](https://mlsanity.com/paper/2203.04286/) (2022)
- [Variational Zero-shot Multispectral Pansharpening](https://mlsanity.com/paper/2407.06633/) (2024)
- [FusionRF: High-Fidelity Satellite Neural Radiance Fields from Multispectral and Panchromatic Acquisitions](https://mlsanity.com/paper/2409.15132/) (2024)
- [CMT: Cross Modulation Transformer with Hybrid Loss for Pansharpening](https://mlsanity.com/paper/2404.01121/) (2024)
- [PanAdapter: Two-Stage Fine-Tuning with Spatial-Spectral Priors Injecting for Pansharpening](https://mlsanity.com/paper/2409.06980/) (2024)
- [Neural Shr"odinger Bridge Matching for Pansharpening](https://mlsanity.com/paper/2404.11416/) (2024)
- [Multi-Head Attention Residual Unfolded Network for Model-Based Pansharpening](https://mlsanity.com/paper/2409.02675/) (2024)
- [LAConv: Local Adaptive Convolution for Image Fusion](https://mlsanity.com/paper/2107.11617/) (2021)
- [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)
- [Deep dual stream residual network with contextual attention for pansharpening of remote sensing images](https://mlsanity.com/paper/2207.12004/) (2022)
- [Learning to Pan-sharpening with Memories of Spatial Details](https://mlsanity.com/paper/2306.16181/) (2023)
- [CAT: A Conditional Adaptation Tailor for Efficient and Effective Instance-Specific Pansharpening on Real-World Data](https://mlsanity.com/paper/2504.10242/) (2025)
- [Fast Kernel-Space Diffusion for Remote Sensing Pansharpening](https://mlsanity.com/paper/2505.18991/) (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)
- [Robust Non-negative Proximal Gradient Algorithm for Inverse Problems](https://mlsanity.com/paper/2510.23362/) (2025)
- [PAKAN: Pixel Adaptive Kolmogorov-Arnold Network Modules for Pansharpening](https://mlsanity.com/paper/2603.15109/) (2026)
- [Fast Model-guided Instance-wise Adaptation Framework for Real-world Pansharpening with Fidelity Constraints](https://mlsanity.com/paper/2604.08903/) (2026)
- [CGFformer: Cluster-Guidance Frequency Transformer for Pansharpening](https://mlsanity.com/paper/2605.01490/) (2026)

## Abstract (arXiv)

Pansharpening aims to fuse a multispectral (MS) image with an associated panchromatic (PAN) image, producing a composite image with the spectral resolution of the former and the spatial resolution of the latter. Traditional pansharpening methods can be ascribed to a unified detail injection context, which views the injected MS details as the integration of PAN details and band-wise injection gains. In this work, we design a detail injection based CNN (DiCNN) framework for pansharpening, with the MS details being directly formulated in end-to-end manners, where the first detail injection based CNN (DiCNN1) mines MS details through the PAN image and the MS image, and the second one (DiCNN2) utilizes only the PAN image. The main advantage of the proposed DiCNNs is that they provide explicit physical interpretations and can achieve fast convergence while achieving high pansharpening quality. Furthermore, the effectiveness of the proposed approaches is also analyzed from a relatively theoretical point of view. Our methods are evaluated via experiments on real-world MS image datasets, achieving excellent performance when compared to other state-of-the-art methods.