Adaptive regularized scheme for remote sensing image fusion

We propose an adaptive regularized algorithm for remote sensing image fusion based on variational methods. In the algorithm, we integrate the inputs using a “grey world” assumption to achieve visual uniformity. We propose a fusion operator that can automatically select the total variation (TV)–L₁ term for edges and L₂-terms for non-edges. To implement our algorithm, we use the steepest descent method to solve the corresponding Euler–Lagrange equation. Experimental results show that the proposed algorithm achieves remarkable results.