Automatic Machine Learning based Real Time Multi-Tasking Image Fusion

Imaging systems work diversely in the image processing domain, and each system contains specific characteristics. We are developing models to fuse images from different sensors and environments to get promising outcomes for different computer vision applications. The multiple unified models have been developed for multiple tasks such as multi-focus (MF), multi-exposure (ME), and multi-modal (MM) image fusion. The careful tuning of such models is required to get optimal results, which are still not applicable to diverse applications. We propose an automatic machine learning (AML) based multi-tasking image fusion approach to overcome this problem. Initially, we evaluate source images with AML and feed them to the task-based models. Then, the source images are fused with the pre-trained and fine-tuned models. The experimental results authenticate the consequences of our proposed approach compared to generic approaches.