Finding a fusion metric that best reflects human observer preference

A perception test determined which of several image fusion metrics best correlates with relative observer preference. Many fusion techniques and fusion metrics have been proposed, but there is a need to relate them to a human observer's measure of image quality. LWIR and MWIR images were fused using techniques based on the Discrete Wavelet Transform (DWT), the Shift-Invariant DWT (SIDWT), Gabor filters, Pixel averaging, and Principal Component Analysis (PCA). Two different sets of fused images were generated from urban scenes. The quality of the fused images was then measured using the mutual information metric (MINF), fusion quality index (FQI), edge-dependent fusion quality index (EDFQI), weighted-fusion quality index (WFQI), and the mean-squared errors between the fused and source images (MS(F-L), MS(F-M)). A paired-comparison perception test determined how observers rated the relative quality of the fused images. The observers based their decisions on the noticeable presence or absence of information, blur, and distortion in the images. The observer preferences were then correlated with the fusion metric outputs to see which metric best represents observer preference. The results of the paired comparison test show that the mutual information metric most consistently correlates well with the measured observer preferences.