Researchers are developing methods to improve image deconvolution in microscopy, with applications in confocal and STED microscopy. Various approaches are being explored, including the use of neural networks, physics-informed techniques, and Bayesian-based methods. These advancements aim to achieve high-fidelity, super-resolution imaging, with some methods also focusing on live cell application and widefield fluorescence microscopy.