One approach to overcome the detection limitations of RM is through the application of machine learning (ML) procedures, which have recently shown significant potential in enhancing microscopy signal processing capabilities. The most prominent examples include supervised and self-supervised ML methods for denoising and image restoration based on deep learning architectures, including convolutional neural networks, generative adversarial networks, and autoencoders. For instance, in a recent study, researchers employed a U-Net architecture with the Noise2Noise framework to create Topaz-Denoise, trained on thousands of micrographs collected from various microscopes and cameras across a wide range of imaging conditions, achieving a 100-fold signal-to-noise ratio improvement over raw cryo-electron microscopy images. This approach could, in principle, be adapted to existing RM platforms. However, implementation would require the creation of a training dataset containing paired noisy optical images and their corresponding denoised versions, which is unfortunately not accessible prior to instrumentation improvements in RM. Furthermore, even with improved instrumentation, each individual RM platform and case study would necessitate the creation of a new training dataset, making this approach impractical, time-consuming, and costly.