The field of imaging and analysis is moving towards the integration of multiple modalities to enhance the accuracy and resolution of results. This is evident in the development of novel frameworks and methods that combine different imaging techniques, such as deep learning-based denoising, dual-modality fusion networks, and joint variational frameworks. These approaches have shown significant improvements in image quality, noise reduction, and feature extraction, enabling the analysis of complex systems and materials at the nanoscale. Noteworthy papers in this regard include the deep learning denoising framework, which has been shown to preserve physical fidelity and reduce uncertainty in model learning, and the DM-QPMNet, which demonstrates substantial improvements over monolithic concatenation and single-modality baselines for cell segmentation. The balanced multimodal learning approach via mutual information is also noteworthy, as it addresses modality imbalance and improves overall multimodal model performance.