  In recent years, numerous ideas have emerged for designing a mutually
reinforcing mechanism or extra stages for the image fusion task, ignoring the
inevitable gaps between different vision tasks and the computational burden. We
argue that there is a scope to improve the fusion performance with the help of
the FusionBooster, a model specifically designed for the fusion task. In
particular, 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 result
produced by a backbone method, the probe units assess the fused image and
divide the results 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 the role of 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 in conjunction with lightweight autoencoder
models to formulate the learning task, with marginal computational complexity
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 code will be publicly
available at https://github.com/AWCXV/FusionBooster.
