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：

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基于稀疏表示的医学图像融合的最新进展


医学图像融合旨在将不同成像方式捕获的多源信息结合起来，对于医疗专业人员的精确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）的方法因其强大的图像表示能力而成为医学图像融合领域非常活跃的方向。在本文中，我们主要概述了基于 SR 的医学图像融合的最新进展，从传统的局部和单分量 SR 方法到最新的全局和多分量 SR 方法。此外，还提出了这个方向仍然存在的几个主要挑战，并讨论了一些未来的前景。