Medical image fusion via decoupled representation and component-wise regularization learning

Medical image fusion plays an important role in the precise diagnosis, treatment planning, and follow-up studies of various diseases. While tremendous improvements in medical image fusion based on convolution sparse coding have been achieved, existing methods are still limited by the intractable redundancy information interaction between source medical images. In this paper, we propose an easy yet effective representation and regularization learning method based on decomposed components scheme with high competitive performance. We construct more compact information interactions by decoupled representation learning, which simultaneously mitigates the problem of redundancy in fusion component entanglement. And then two different regularization operators are adaptively exploited to depict two different components separately, which describe the structural-inspired difference based on the decoupled principle. Furthermore, we combine the alternating direction method of multipliers (ADMM) algorithm and the conjugate gradient (CG) method to optimize our proposed model. Our experiments demonstrate that our proposed method has significant improvements in efficiency and fusion performance against the state-of-the-art methods.