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Optimizing multiresolution pixel-level image fusion

A number of pixel level image fusion schemes have been proposed in the past which combine registered input sensor images into a single fused output image. The two general objectives that underpin the operations of these schemes are a) the transfer of all visually important information form input images into a fused image and b) the minimization of undesirable distortions and artifacts which may be generated in the fused image. Fusion is usually achieved by i) the decomposition of input images into representations of their spectral bands and ii) a selection process which transfers information from input bands to yield the required representation of a single fused output image. Furthermore, decomposition is often based on multi-resolution pyramidal representations and the selection process operates on corresponding input image pyramidal levels using selection templates which focus on local spectral characteristics. The performance of such a multi-resolution pixel level image fusion system depends primarily on the actual decomposition and selection algorithms used. Thus for a given decomposition selection arrangement, fusion performance is dependent on the pyramid size (i.e. number of level) and template size. Pyramid and template sizes on the other hand greatly influence the system's computational complexity. This paper is concerned with the performance optimization/characterization of several multi- resolution image fusion schemes, in general and with performance/ complexity trade-offs in particular. Performance is measured using a subjectively meaningful, objective fusion metric which has been proposed recently by authors and which is based on the preservation of image edge information. Thus fusion systems based on derivatives of Gaussian low-pass pyramid and the Discrete Wavelet transform are examined and their performances versus decomposition/selection parameters are defined and compared. The performance/algorithmic complexity results presented for these multi-resolution fusion systems highlight clearly their strengths and weaknesses.