Image Fusion in the Multi-Scale Transforms Domain

The research presented in this book is concerned with the problem of multi-sensor pixel-level image fusion. Generally, the image fusion's task is used for three different applications consisting of fusing the multi-focus images, fusing the infrared and visible images, and fusing the multi-spectral and panchromatic images. We formulate the image fusion process as a two-class problem: in focus and out of focus classes, in which the decision map for selecting important coefficients between input images is obtained using two-class fisher classifier. In the proposed method for fusing infrared and visible images, first, the input images are decomposed using dual-tree discrete wavelet transform and then, we use a dissimilarity measure of source images to combine three different fusion rules for selecting high frequency wavelet coefficients between source images. Finally, a new method for fusion of remote sensing images is proposed. In this method, the aim is to improve spatial and spectral quality of the fused image, simultaneously. We use the shiftable contourlet transform and multi-objective particle swarm optimization for this purpose.

Autorentext

Jamal Saeedi received his M.Sc. and Ph.D. degrees in Electronic Engineering from Amirkabir University of Tehran, Iran in 2010 and 2015, respectively. He works in the field of signal and image processing, particularly in information fusion, pattern recognition, radar systems design, radar jamming and deception, and synthetic aperture radar imaging.