The PanAdapter framework, designed for pansharpening, can be effectively extended to other image fusion tasks by leveraging its two-stage fine-tuning strategy and the concept of spatial-spectral prior extraction. For instance, in tasks such as multi-focus image fusion or medical image fusion, the framework can be adapted by modifying the input modalities to suit the specific characteristics of the images being fused.
Modality Adaptation: The framework can be tailored to handle different types of images, such as infrared and visible light images in surveillance applications or different spectral bands in remote sensing. By adjusting the Local Prior Extraction (LPE) module to extract relevant features from these modalities, the framework can maintain its effectiveness.
Task-Specific Priors: The two-stage training strategy can be employed to fine-tune pre-trained models on datasets specific to the new fusion tasks. For example, in medical imaging, the first stage could focus on extracting anatomical features from MRI and CT scans, while the second stage could integrate these features into a unified representation.
Cascaded Adapters: The use of cascaded adapters can be generalized to fuse features from various sources, allowing for the integration of multi-scale information. This flexibility can enhance the performance of the framework across diverse image fusion scenarios, such as enhancing low-light images or combining images from different sensors.
Evaluation Metrics: The framework can also incorporate evaluation metrics specific to the new tasks, ensuring that the fine-tuning process is aligned with the desired outcomes of the image fusion, such as improved clarity or detail preservation.
By adapting the PanAdapter framework in these ways, it can serve as a robust solution for a variety of image fusion tasks, thereby broadening its applicability and impact in the field of image processing.