# Quaternion Sparse Decomposition for Multi-focus Color Image Fusion

arXiv: https://arxiv.org/abs/2505.02365 · HTML: https://mlsanity.com/paper/2505.02365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 5 May 2025.

## Builds on (most cited first, 10 of 10)

- [Task-Customized Mixture of Adapters for General Image Fusion](https://mlsanity.com/paper/2403.12494/) (2024)
- [Image Fusion via Vision-Language Model](https://mlsanity.com/paper/2402.02235/) (2024)
- [MFFW: A new dataset for multi-focus image fusion](https://mlsanity.com/paper/2002.04780/) (2020)
- [Quaternion Gradient and Hessian](https://mlsanity.com/paper/1406.3587/) (2014)
- [Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy](https://mlsanity.com/paper/2012.14678/) (2020)
- [SAMF: Small-Area-Aware Multi-focus Image Fusion for Object Detection](https://mlsanity.com/paper/2401.08357/) (2024)
- [Generation and Recombination for Multifocus Image Fusion with Free Number of Inputs](https://mlsanity.com/paper/2309.04657/) (2023)
- [A general framework for constrained convex quaternion optimization](https://mlsanity.com/paper/2102.02763/) (2021)
- [MFIF-GAN: A New Generative Adversarial Network for Multi-Focus Image Fusion](https://mlsanity.com/paper/2009.09718/) (2020)
- [Quaternion higher-order singular value decomposition and its applications in color image processing](https://mlsanity.com/paper/2101.00364/) (2021)

## Abstract (arXiv)

Multi-focus color image fusion refers to integrating multiple partially focused color images to create a single all-in-focus color image. However, existing methods struggle with complex real-world scenarios due to limitations in handling color information and intricate textures. To address these challenges, this paper proposes a quaternion multi-focus color image fusion framework to perform high-quality color image fusion completely in the quaternion domain. This framework introduces 1) a quaternion sparse decomposition model to jointly learn fine-scale image details and structure information of color images in an iterative fashion for high-precision focus detection, 2) a quaternion base-detail fusion strategy to individually fuse base-scale and detail-scale results across multiple color images for preserving structure and detail information, and 3) a quaternion structural similarity refinement strategy to adaptively select optimal patches from initial fusion results and obtain the final fused result for preserving fine details and ensuring spatially consistent outputs. Extensive experiments demonstrate that the proposed framework outperforms state-of-the-art methods.