Tunable-Q contourlet-based multi-sensor image fusion

We propose a tunable-Q contourlet transform for multi-sensor texture-image fusion. The standard contourlet transform (CT) uses a multiscale pyramid to decompose an image into frequency channels that have the same bandwidth on a logarithmic scale. This low-Q decomposition scheme is not suitable for the representation of rich-texture images, in which there are numerous edges and thus rich intermediate- and high-frequency components in the frequency domain. By using a tunable decomposition parameter, the Q-factor of our tunable-Q CT can be efficiently tuned. With an acceptable redundancy, the tunable-Q CT is also anti-aliasing, and its basis is sharply localized in the desired area of the frequency domain. Experimental results show that image fusion based on the tunable-Q CT can not only reasonably preserve spectral information of multispectral images, but can also effectively extract texture details from high-resolution images. The proposed method easily outperforms fusion based on the nonsubsampled wavelet transform or on the nonsubsampled CT in both visual quality and objective evaluation. (C) 2012 Elsevier B.V. All rights reserved.