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We present a new technique for fusing together an arbitrary number of aligned images into a single color or intensity image. We approach this fusion problem from the context of Multidimensional Scaling (MDS) and describe an algorithm that preserves the relative distances between pairs of pixel values in the input (vectors of measurements) as perceived differences in a color image. The two main advantages of our approach over existing techniques are that it can incorporate user constraints into the mapping process and allows adaptively compressing or exaggerating features in the input in order to make better use of the output's limited dynamic range. We demonstrate these benefits by showing applications in various scientific domains and comparing our algorithm to previously proposed techniques.