Image fusion is capable of processing multiple heterogeneous images acquired by single or multi-sensor imaging systems for an improved interpretation of the targeted object or scene. A diversity of applications have benefited from the fusion of multi-sensor images through a more reliable and comprehensive fused result. Likewise, numerous approaches to fuse multi-sensor images have been proposed and published in literature. However, due to a lack of benchmark resources and commonly accepted assessment measures, it is hard to identify the significance of new image fusion algorithms and implementations. This paper reviews and categorizes recent algorithms for image fusion and performance assessment based on reported comparative results. We recommend using non-parametric statistical tests to verify the performance of the pixel-level fusion algorithms. Furthermore, a comprehensive evaluation of 40 fusion algorithms from recently published results is conducted to demonstrate the significance of these algorithms in terms of statistical analyses within their respective applications. Although the results of these performance tests are limited by available data sets, baseline algorithms, and selected assessment metrics; it is a critical step for comparative image fusion research. This paper aims to advance image fusion development by creating a complete inventory of state-of-the-art image fusion techniques and advocating statistical comparison tests to avoid unnecessary duplication of development efforts. Establishing a benchmark study for image fusion is critical for performance comparisons of contemporary methods. © 2017 Elsevier B.V.