Variational bimodal image fusion with data-driven tight frame

Abstract

The purpose of multimodal image fusion is to combine information from different modal images of the same investigated object and create an image that is suitable for human vision and subsequent image processing. This paper proposes a three-step method for bimodal image fusion. A tight frame system is first adaptively learned from bimodal images for capturing source images features as much as possible. Further, a fused coefficient set is constructed by integrating the frame coefficients from both modalities. Finally, a variational model is designed to reconstruct a fused image based on the fused coefficients, and the intensity information of those smooth regions. The alternating iteration scheme and alternating direction method of multipliers are used to solve the resulted variational problems. Numerical experiments on multimodal medical image fusion and multifocused natural image fusion indicate that the proposed approach outperforms some existing methods.