Fractional-Order Variational Image Fusion and Denoising Based on Data-Driven Tight Frame

Multi-modal image fusion can provide more information, which improves the quality for subsequent processing tasks. Because images acquired using photon counting devices always suffer from Poisson noise, this paper proposes a new three-step method based on fractional-order variational and data-driven tight frame to solve problem of multi-modal corrupted by noise. Thus, article obtains fused high-quality while removing The proposed model be solved split Bregman algorithm has significant stability fast convergence. numerical results various modal show excellent performance in terms evaluation metrics visual quality. Extensive experiments demonstrate that our outperforms state-of-the-art methods with