In contrast to supervised approaches, we opt for unsupervised ML methods, which can operate without requiring pre-labeled training data. Various unsupervised techniques, including K-means clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and dimensionality reduction methods (Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) ), have been successfully applied to microscopy signal processing in materials science contexts. While it is challenging to quantify the improvement factor in these cases due to the absence of denoised reference images for comparison, some studies attempt to provide measurable metrics. For instance, research employing non-local means denoising with automatically estimated noise standard deviation, followed by K-means segmentation of secondary electron microscopy (SEM) images, yielded results identical to manual human labeling when compared. Another example utilizing 'one-shot' learning for automatic denoising and segmentation of Raman microscopy images based on convolutional autoencoders demonstrated a two-fold improvement in signal-to-noise ratio. This enhancement, while less pronounced than for supervised methods, should still significantly improve state-of-the-art RM detection capabilities and potentially unlock challenges associated with PSZ detection.