Statistical analysis of the performance assessment results for pixel-level image fusion

Pixel-level image fusion (PLIF) performance assessment includes information theory, feature-based, structural similarity, and perception-based objective metrics. However, to relate these metrics to human understanding requires subjective metrics. This paper proposes to use statistical analyses to assess PLIF performance over objective and subjective metrics. Non-parametric tests are applied to the subjective and objective assessment data from three multi-resolution image fusion methods using visual and infrared images. The tests can offer the performance information about the fusion algorithm at a designated significance level. Statistical analysis of PLIF facilitates the establishment of a baseline for the research in image fusion and serves as a statistical validation for proposing, comparing, and adopting a new PLIF algorithm.