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In this paper, we present a nonstationary Markov random field (MRF) fusion model for the color display of hyperspectral images. The proposed fusion or dimensionality reduction model is derived from the preservation of spectral distance criterion. This quantitative metric of good dimensionality reduction and meaningful visualization allows us to derive an appealing fusion model of high-dimensional spectral data, expressed as a Gibbs distribution or a nonstationary MRF model defined on a complete graph. In this framework, we propose a computationally efficient coarse-to-fine conjugate-gradient optimization method to minimize the cost function related to this energy-based fusion model. The experiments reported in this paper demonstrate that the proposed visualization method is efficient (in terms of preservation of spectral distances and discriminality of pixels with different spectral signatures) and performs well compared to the best existing state-of-the-art multidimensional imagery color display methods recently proposed in the literature.