Classification-based image-fusion framework for compressive imaging

Abstract

We propose a novel image-fusion framework for compressive imaging (CI), which is a new technology for simultaneous sampling and compressing of images based on the principle of compressive sensing (CS). Unlike previous fusion work operated on conventional images, we directly perform fusion on the measurement vectors from multiple CI sensors according to the similarity classification. First, we define a metric to evaluate the data similarity of two given CI measurement vectors and present its potential advantage for classification. Second, the fusion rules for CI measurement vectors in different similarity types are investigated to generate a comprehensive measurement vector. Finally, the fused image is reconstructed from the combined measurements via an optimization algorithm. Simulation results demonstrate that the reconstructed images in our fusion framework are visually more appealing than the fused images using other fusion rules, and our fusion method for CI significantly saves computational complexity against the fusion-after-reconstruction scheme.