Image fusion using hierarchical PCA.

Abstract

In this paper we propose image fusion algorithm using hierarchical PCA. Image fusion is a process of combining two or more images (which are registered) of the same scene to get the more informative image. Hierarchical multiscale and multiresolution image processing techniques, pyramid decomposition are the basis for the majority of image fusion algorithms. Principal component analysis (PCA) is a well-known scheme for feature extraction and dimension reduction and is used for image fusion. We propose image fusion algorithm by combining pyramid and PCA techniques and carryout the quality analysis of proposed fusion algorithm without reference image. There is an increasing need for the quality analysis of the fusion algorithms as fusion algorithms are data set dependent. Subjective analysis of fusion algorithm using hierarchical PCA is done by considering the opinion of experts and non experts and for quantitative quality analysis we use different quality metrics. We demonstrate fusion using pyramid, wavelet and PCA fusion techniques and carry out performance analysis for these four fusion methods using different quality measures for variety of data sets and show that proposed image fusion using hierarchical PCA is better for the fusion of multimodal imaged. Visible inspection with quality parameters are used to arrive at a fusion results.

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