The probably most straightforward way to build a fused image of several input frames is performing the fusion as a weighted superposition of all input frames.
The optimal weighting coefficients, with respect to information content and redundancy removal, can be determined by a principal component analysis (PCA) of all input intensities. By performing a PCA of the covariance matrix of input intensities, the weightings for each input frame are obtained from the eigenvector corresponding to the largest eigenvalue.
A similar procedure is the linear combination of all inputs in a pre-chosen colorspace (eg. R-G-B or H-S-V), leading to a false color representation of the fused image.