The aim of this paper is twofold: (i) to define appropriate metrics which measure the effects of input sensor noise on the performance of signal-level image fusion systems and (ii) to employ these metrics in a comparative study of the robustness of typical image fusion schemes whose inputs are corrupted with noise. Thus system performance metrics for measuring both absolute and relative degradation in fused image quality are proposed when fusing noisy input modalities. A third metric, which considers fusion of noise patterns, is also developed and used to evaluate the perceptual effect of noise corrupting homogenous image regions (i.e. areas with no salient features). These metrics are employed to compare the performance of different image fusion methodologies and feature selection/information fusion strategies operating under noisy input conditions. Altogether, the performance of seventeen fusion schemes is examined and their robustness to noise considered at various input signal-to-noise ratio values for three types of sensor noise characteristics.