基于最优配准和奇异值分解算法的图像融合程序研究

D. Repperger, A. Pinkus, K. Farris, R. G. Roberts, R. Sorkin
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引用次数: 8

摘要

采用两种优化方法对图像数据进行配准和融合。该配准问题采用奇异值分解(SVD)方法。实际图像融合采用最大似然决策规则方法。该ML过程通过误差平方(卡方)进行了修改,但提供了一种接受和拒绝候选图像以包含到融合图像中的最佳方法。数值仿真结果表明了该方法的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation of image fusion procedures using optimal registration and SVD algorithms
Two types of optimization procedures are employed to both register and fuse image data. The registration problem utilizes a singular value decomposition (SVD) method. The actual fusion of the constituent images employs a maximum likelihood (ML) decision rule methodology. This ML procedure is modified through the square of the errors (Chi square) but provides an optimal method to accept and reject candidate images for inclusion into the fused images. Numerical simulations show the applicability of the method.
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