Image fusion in compressed sensing

This paper proposes an efficient image fusion scheme for compressed sensing (CS) imaging, in which fusion is performed on the random projections before reconstruction. Specifically, the measurements of multiple input images are fused into composite measurements via weighted average, in which the weights are calculated based on entropy metrics of the original measurements. Then the fused image with transformation coefficients in a selected basis is reconstructed from the composite measurements by the gradient projection for sparse reconstruction (GPSR) algorithm. The proposed scheme is implemented in a block-based CS framework. Simulation results show that our scheme provides promising fusion performance with a low computational complexity.