# Rethinking Few-Shot Image Fusion: Granular Ball Priors Enable General-Purpose Deep Fusion

arXiv: https://arxiv.org/abs/2504.08937 · HTML: https://mlsanity.com/paper/2504.08937/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 60 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 17 of 17)

- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [DenseFuse: A Fusion Approach to Infrared and Visible Images](https://mlsanity.com/paper/1804.08361/) (2018)
- [Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack](https://mlsanity.com/paper/1811.09310/) (2018)
- [Equivariant Multi-Modality Image Fusion](https://mlsanity.com/paper/2305.11443/) (2023)
- [DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs](https://mlsanity.com/paper/1712.07384/) (2017)
- [CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion](https://mlsanity.com/paper/2211.10960/) (2022)
- [Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption](https://mlsanity.com/paper/2501.10761/) (2025)
- [Medical Image Fusion: A survey of the state of the art](https://mlsanity.com/paper/1401.0166/) (2013)
- [Benchmarking and Comparing Multi-exposure Image Fusion Algorithms](https://mlsanity.com/paper/2007.15156/) (2020)
- [One Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image Fusion](https://mlsanity.com/paper/2502.19854/) (2025)
- [TextFusion: Unveiling the Power of Textual Semantics for Controllable Image Fusion](https://mlsanity.com/paper/2312.14209/) (2023)
- [UltraFusion: Ultra High Dynamic Imaging using Exposure Fusion](https://mlsanity.com/paper/2501.11515/) (2025)
- [ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning](https://mlsanity.com/paper/2312.07943/) (2023)
- [A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set](https://mlsanity.com/paper/2201.03349/) (2022)
- [Generation and Recombination for Multifocus Image Fusion with Free Number of Inputs](https://mlsanity.com/paper/2309.04657/) (2023)
- [SMLNet: A SPD Manifold Learning Network for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2411.10679/) (2024)

## Abstract (arXiv)

In image fusion tasks, the absence of real fused images as supervision signals poses significant challenges for supervised learning. Existing deep learning methods typically address this issue either by designing handcrafted priors or by relying on large-scale datasets to learn model parameters. Different from previous approaches, this paper introduces the concept of incomplete priors, which formally describe handcrafted priors at the algorithmic level and estimate their confidence. Based on this idea, we couple incomplete priors with the neural network through a sample-level adaptive loss function, enabling the network to learn and re-infer fusion rules under conditions that approximate the real fusion process.To generate incomplete priors, we propose a Granular Ball Pixel Computation (GBPC) algorithm based on the principles of granular computing. The algorithm models fused-image pixels as information units, estimating pixel weights at a fine-grained level while statistically evaluating prior reliability at a coarse-grained level. This design enables the algorithm to perceive cross-modal discrepancies and perform adaptive inference.Experimental results demonstrate that even under few-shot conditions, a lightweight neural network can still learn effective fusion rules by training only on image patches extracted from ten image pairs. Extensive experiments across multiple fusion tasks and datasets further show that the proposed method achieves superior performance in both visual quality and model compactness. The code is available at: https://github.com/DMinjie/GBFF