Medical image fusion, which aims to combine multi-source information captured by different imaging modalities, is of great significance to medical professionals for precise diagnosis and treatment. In the last decade, sparse representation (SR)-based approach has emerged as a very active direction in the field of medical image fusion, due to its powerful ability for image representation. In this paper, we mainly present an overview of the recent advances achieved in SR-based medical image fusion, ranging from the conventional local and single-component SR-based methods to the latest global and multi-component SR-based methods. In addition, several major challenges remained in this direction are presented and some future prospects are discussed.

中文翻译：

---

基于稀疏表示的医学图像融合的最新进展

医学图像融合旨在融合通过不同成像方式捕获的多源信息，对医学专业人员进行精确的诊断和治疗具有重要意义。在过去的十年中，基于稀疏表示（SR）的方法由于其强大的图像表示能力已经成为医学图像融合领域中非常活跃的方向。在本文中，我们主要介绍基于SR的医学图像融合技术的最新进展，从传统的基于局部和单组分SR的方法到最新的基于全局和多组分SR的方法。此外，提出了在这个方向上仍然存在的几个主要挑战，并讨论了一些未来的前景。