The field of microscopy image analysis is rapidly advancing with the development of innovative machine learning approaches. Researchers are focusing on improving image reconstruction, super-resolution, and noise reduction techniques to enhance the quality and accuracy of microscopy images. Self-supervised learning methods are being explored to classify synapse types and learn angular-aware representations for light field microscopy. Physics-informed synthetic data generation pipelines are being used to train models for image restoration and super-resolution in scanning tunneling microscopy. Vision transformers are being applied to reconstruct thin liquid film thickness profiles from interference patterns in real-time. These advancements have the potential to significantly improve the throughput and accuracy of microscopy experiments, enabling new possibilities for continuous monitoring and non-invasive diagnosis of various conditions. Noteworthy papers include: From Pixels to Views, which introduces a self-supervised task to learn angular priors for light field microscopy, and Towards Real-Time Inference of Thin Liquid Film Thickness Profiles, which presents a vision transformer-based approach for real-time inference of thin liquid film thickness profiles. Additionally, Generative Image Restoration and Super-Resolution using Physics-Informed Synthetic Data for Scanning Tunneling Microscopy demonstrates a physics-informed synthetic data generation pipeline for image repair and super-resolution, and ResMatching: Noise-Resilient Computational Super-Resolution via Guided Conditional Flow Matching presents a novel computational super-resolution method that uses guided conditional flow matching to learn improved data-priors.