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.

翻译：近年来，众多研究聚焦于为图像融合任务设计互增强机制或额外阶段，却忽视了不同视觉任务间必然存在的差距以及计算负担。我们认为，通过专门为融合任务设计的FusionBooster模型，仍有提升融合性能的空间。具体而言，我们的增强器基于由信息探针控制的“分而治之”策略。该增强器由三个构建模块组成：探针单元、增强层与组装模块。在主方法生成结果后，探针单元评估融合图像并根据信息含量对结果进行划分。这有助于识别缺失信息，并为后续恢复奠定基础。对退化组件的恢复及融合引导由增强层负责。最后，组装模块将这些优化后的组件整合以输出最终结果。我们采用简洁的重构损失函数配合轻量级自编码器模型构建学习任务，仅引入微小的计算复杂度增量。在多种融合任务及下游检测任务中获得的实验结果表明，所提出的FusionBooster显著提升了性能。我们的代码将开源至 https://github.com/AWCXV/FusionBooster。