An application of compressive sensing for image fusion

Compressive sensing (CS) has inspired significant interests because of its compressive capability and lack of complexity on the sensor side. This paper introduces a novel framework of image fusion based on the CS principle. First, we present a study of three sampling patterns and investigate their performance on CS reconstruction. We then propose a novel image fusion algorithm by using an improved sampling pattern. Finally, the CS-based image fusion approach is applied to various image modalities and evaluated both visually and in terms of fusion quality metrics. The simulations demonstrate that CS-based image fusion has a number of perceived advantages in comparison with image fusion in the multiresolution (MR) domain, providing a truly different and more advanced way for fusing multimodality images.