Unrolling Alternating Direction Method of Multipliers for Visible and Infrared Image Fusion

Abstract

In this paper a new infrared and visible image fusion (IVIF) method which combines the advantages of optimization and deep learning based methods is proposed. This model takes the iterative solution used by the alternating direction method of the multiplier (ADMM) optimization method, and uses algorithm unrolling to obtain a high performance and efficient algorithm. Compared with traditional optimization methods, this model generates fusion with 99.6% improvement in terms of image fusion time, and compared with deep learning based algorithms, this model generates detailed fusion images with 99.1% improvement in terms of training time. Compared with the other state-of-the-art unrolling based methods, this model performs 26.7% better on average in terms of Average Gradient (AG), Cross Entropy (CE), Mutual Information (MI), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Loss (SSIM) metrics with a minimal testing time cost.