A general metric and parallel framework for adaptive image fusion in clusters

This article is dedicated to techniques and theories of image fusion in automatic ways and addresses two issuesthe parameter setting and quality assessment. Optimal parameters are in demand for specific applications or comparison between fusion methods because, as basic evidence, different parameters bring different fusion effects varying over a large range. In this paper, we propose a general framework of online parameter training to search optimal values that best suit input images. Furthermore, we optimized the compute-intensive training process using parallelization and genetic algorithm, as well as patches extraction. We also propose a metricspatial and spectral distortionas the learning target. The spatial and spectral distortion is a fuzzy combination of mean potential energy measuring spatial distortion and Q4 measuring spectral distortion. Optimization validation on weighted Gram-Schmidt fusion indicated linear or superlinear acceleration ability, which proved that the proposed learning framework can speed up the learning process of image fusion to an acceptable time, and can thus be applied to high-performance platforms to process large volumes of data. Copyright (c) 2013 John Wiley & Sons, Ltd.