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

Abstract

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