FusionBooster: A Unified Image Fusion Boosting Paradigm

Numerous ideas have emerged for designing fusion rules in the image fusion field. Essentially, all the existing formulations try to manage the diverse levels of information communicated by the source images to achieve the best fusion result. We argue that there is a scope for improving the performance of existing methods further with the help of FusionBooster, a fusion guidance method proposed in this paper. Our booster is based on the divide and conquer strategy controlled by an information probe. The booster is composed of three building blocks: the probe units, the booster layer, and the assembling module. Given the embedding produced by a backbone method, the probe units assess the source images and divide them according to their information content. This is instrumental in identifying missing information, as a step to its recovery. The recovery of the degraded components along with the fusion guidance are embedded in the booster layer. Lastly, the assembling module is responsible for piecing these advanced components together to deliver the output. We use concise reconstruction loss functions and lightweight models to formulate the network, with marginal computational increase. The experimental results obtained in various fusion tasks, as well as downstream detection tasks, consistently demonstrate that the proposed FusionBooster significantly improves the performance. Our codes will be publicly available on the project homepage.